a l networks stemu s t p a l...doctor of philosophy school of teacher education and leadership...
TRANSCRIPT
AN ANALYSIS OF LEARNING NETWORKS OF STEM UNDERGRADUATE STUDENTS
TO PROMOTE ACTIVE LEARNING
Iwona A. CzaplinskiMPhil
MA
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Teacher Education and Leadership
Faculty of Education
Queensland University of Technology
2020
There is also the division that separates merely conscious
creatures from self-conscious creatures like us. Only the second
have a genuine “first-person” perspective, from which to
distinguish how things seem to me from how they seem to you.
Creatures with “I” thoughts have an ability to relate to others of
their kind that sets them apart from the rest of nature, and many
thinkers (Kant, Fichte, and Hegel pre-eminently ) believe that it
is this fact, not the fact of consciousness per se, that creates or
reveals the central mysteries of the human condition.
Sir Roger Scruton, On Human Nature, p. 29
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning i
Keywords
Activity-centred analysis and design framework, active learning, autonomous learner,
affordance, connectivism, connected learning, connected curriculum, design patterns,
ecological systems theory, ecological curriculum and learning design framework of
connected epistemic domains, epistemic fluency, epistemic domains, epistemic
design, epistemic practice, expert learner, frame of reference, functional context,
illusion of competence, interference of perceptions, learning design, learning
networks, networked learning, novice learner, personal learning networks, personal
learning environments, self-direction, self-regulation, science technology engineering
and mathematics education
ii An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
Abstract
The ability of actively learning (Bjork, Dunlosky & Kornell, 2013) over a prolonged
period of time within digital and distributed learning environments is rapidly becoming
the learning skill of the future. Furthermore, active learning enables epistemic fluency
(Markauskaite & Goodyear, 2017a; 2017b; 2018a; 2018b), the ability of integrating
diverse knowledges and different ways of knowing into professional practices. With a
plethora of formal and informal opportunities for learning, contemporary learners face
a challenge of learning from, with and about surrounding environments using their
learning networks.
This single-case study examines the ways contemporary, first-year university
students, enrolled in STEM-focused disciplines, are using their formal and informal
learning networks and practice networked learning. The thesis also looks at academic
teaching teams’ perceptions of the ways their students were using learning networks
and practicing networked learning.
Research was conducted in two phases using a sequential mixed model with
integrated qualitative instruments to collect the data. First, student and staff
questionnaires (quantitative instrument) containing open-ended questions (integrated
qualitative instruments) were administered to 135 participating students and 8
academic teaching staff members. The research applied quantitative data analysis
procedures (Creswell, 2014) to calculate measures of central tendency (medians), and
to measure variability (ranges) for each individual question. The qualitative data from
open-ended questions/ comments were analysed using an inductive approach to
content analysis. The initial data analysis enabled the identification of preliminary
findings that were next fine-tuned through focus groups conducted with 17 students
and 6 academic teaching staff. The qualitative data obtained through focus groups were
analysed using a deductive approach to content analysis that revealed overarching
concepts and complemented findings resulting from the quantitative data analysis.
Bronfenbrenner’s Bioecological Model of Human Development (Bronfenbrenner,
2005; Bronfenbrenner & Morris, 2006), the concept of learning network (Goodyear &
Carvalho, 2014a, 2014b, 2016), and the notion of affordance (Czaplinski, 2013;
Gibson, 1977; 1979; Norman, 1988; 1999; Good, 2007; Reed, 1996) provided the
research paradigm to interpret the findings, which revealed a series of relationships
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning iii
between the type of learning network, functional context (learning environment) and
social agents’ frames of reference. In addition, the study provided evidence of student
respondents’ low levels of self-regulatory and self-directed learning skills, and of the
relationship between the concept of illusion of competence and the low levels of
students’ awareness of effective learning strategies. Finally, the study constructed the
phenomenon of “interference of student and academic teaching staffs’ perceptions”
and its consequences for both designing learning tasks that use self-regulation and self-
direction, and for the application of connected learning ideas in the curriculum. The
data analysis further pointed to the identified academic teaching staff’s tendency of
making assumptions about their students and the need for improving pedagogical
readiness of academic teaching staff.
As a result of the findings, an ecological curriculum and learning design
framework of connected epistemic domains was constructed as a way of overcoming
identified issues and promoting active learning.
The study contributes to the body of knowledge in research fields such as learning
networks and networked learning, promotion of epistemic fluency for professional
education, curriculum and learning design, academic development, or evidence-based
analyses of learning and teaching approaches in STEM-focused disciplines.
iv An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
Table of Contents
Keywords .................................................................................................................................. i
Abstract .................................................................................................................................... ii
Table of Contents .................................................................................................................... iv
List of Figures ........................................................................................................................ vii
List of Tables......................................................................................................................... viii
List of Abbreviations and Acronyms ....................................................................................... x
Statement of Original Authorship ........................................................................................... xi
Publications and presentations during the candidature .......................................................... xii 1.1.1 Journal articles ................................................................................................... xii 1.1.2 Refereed conference papers ............................................................................... xii 1.1.3 Presentations ..................................................................................................... xiii
Acknowledgements ................................................................................................................ xv
Introduction ...................................................................................... 1
1.1 Problem .......................................................................................................................... 2
1.2 Purpose and aim............................................................................................................. 3
1.3 Objectives and research questions ................................................................................. 4
1.4 Definitions...................................................................................................................... 5
1.5 Rationale for the study ................................................................................................... 8 1.5.1 Learning networks and networked learning......................................................... 8 1.5.2 Epistemically fluent lifelong learners ................................................................ 10 1.5.3 Strategic importance of STEM education.......................................................... 13 1.5.4 Active learning................................................................................................... 14 1.5.5 Learning design ................................................................................................. 15
1.6 Scope of the study ........................................................................................................ 15
1.7 Significance of the study.............................................................................................. 16
1.8 Background to the study .............................................................................................. 18
1.9 Context of the study ..................................................................................................... 19 1.9.1 Functional context of Unit E.............................................................................. 21 1.9.2 Functional context of Unit S.............................................................................. 23
1.10 Researcher reflexivity .................................................................................................. 24
1.11 Thesis Outline .............................................................................................................. 26
Literature Review ........................................................................... 29
2.1 Research paradigm....................................................................................................... 30 2.1.1 Ontology and epistemology of the study ........................................................... 30
2.2 Learning networks........................................................................................................ 40 2.2.1 Activity-Centred Analysis and Design framework............................................ 50 2.2.2 The concept of (learning) affordance................................................................. 55 2.2.3 The expanded definition of learning network.................................................... 59
2.3 Productive networked learning and networked learning practices............................... 60
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning v
2.4 Personal learning environments....................................................................................64
2.5 Connectivism................................................................................................................67
2.6 Connected learning in higher education .......................................................................76
2.7 Active learning .............................................................................................................80 2.7.1 Self-direction ......................................................................................................81 2.7.2 Self-regulation ....................................................................................................85 2.7.3 Novice vs expert .................................................................................................89
2.8 Reflection on Literature................................................................................................93
Research design .............................................................................. 99
3.1 Research Methodology ...............................................................................................100 3.1.1 Overview of dominant research methodologies ...............................................100 3.1.2 The embedded, single-case study design..........................................................104
3.2 Research methods .......................................................................................................105 3.2.1 Mixed methods research...................................................................................105 3.2.2 Data collection techniques................................................................................108 3.2.3 Research participants........................................................................................109 3.2.4 Data collection instruments ..............................................................................114
3.3 Data analysis...............................................................................................................121 3.3.1 Quantitative data analysis.................................................................................121 3.3.2 Qualitative data analysis...................................................................................122
3.4 Trustworthiness of the study.......................................................................................125
3.5 Ethics ..........................................................................................................................129 3.5.1 Data collection protocols and procedures: students .........................................129 3.5.2 Data collection protocols and procedures: academic teaching teams...............131
3.6 Reflection on Research Methodology ........................................................................132
Findings ......................................................................................... 135
4.1 Research Question 1: What learning affordances offered by presupposed learning networks were preceived and taken up by undergraduate STEM students? .........................138
4.1.1 Students’ frames of reference...........................................................................139 4.1.2 Elements of learning networks .........................................................................142
4.2 Research Question 2: What networked learning practices are occuring within the presupposed learning networks? ...........................................................................................188
4.2.1 Learning environments.....................................................................................189 4.2.2 Studying for timetabled activities.....................................................................190 4.2.3 Attendance/ non-attendance at the timetabled activities ..................................193 4.2.4 Catching up with missed content of timetabled activities ................................200 4.2.5 Self-reflection on effectiveness of learning practices.......................................203 4.2.6 Self-modification of learning practices ............................................................205 4.2.7 Self-assessment of respondents’ learning skills ...............................................206 4.2.8 Explicitly teaching effective learning strategies...............................................208
4.3 Research Question 3: What are academic staff participants’ perceptions of students’ uptake of learning affordances and their networked learning practices within presupposed learning networks? ................................................................................................................209
4.3.1 Identified academic staff participants’ frames of reference .............................210 4.3.2 Identified academic staff perceptions of the students’ uptake of learning
affordances offered by learning networks ........................................................211 4.3.3 Identified academic staff perceptions of students’ networked learning
practices............................................................................................................227
vi An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
4.3.4 Focus group findings ....................................................................................... 229
4.4 Reflection on Research Findings ............................................................................... 236
Discussion ...................................................................................... 239
5.1 Research question 1: What learning affordances offered by presupposed learning networks were perceived and taken up by undergraduate STEM students? ........................ 240
5.1.1 Relationship between the type of learning network, the functional context and the uptake of learning affordances ............................................................ 242
5.1.2 Relationship between the type of learning network, frames of reference, and the type of ecological system .................................................................... 245
5.1.3 Relationship between the type of learning network, the type of online tool and the required level of autonomy when using the tool ................................. 248
5.2 What networked learning practices are occurring within the presupposed learning networks? ............................................................................................................................. 250
5.2.1 Low level of awareness of effective learning strategies .................................. 251 5.2.2 Respondents’ low levels of awareness of self-directed learning skills ............ 256
5.3 What are academic staff participants’ perceptions of students’ networked learning practices within the presupposed learning networks? .......................................................... 259
5.3.1 The interference of students and academic teaching staff’s perceptions ......... 260 5.3.2 Academic teaching staff’s assumptions and the need of raising teaching
awareness ......................................................................................................... 261
5.4 Reflection on Discussion ........................................................................................... 264
Conclusions.................................................................................... 267
6.1 Conclusions .................................................................................................................... 267
6.2 Implications.................................................................................................................... 275
6.3 Limitations ..................................................................................................................... 277
6.4 Further research.............................................................................................................. 281
6.5 Closing reflection ........................................................................................................... 283
Appendices .............................................................................................................. 285
A. Questionnaire................................................................................................................... 285 A.1 Student version.................................................................................................... 285 A.2 Staff version ........................................................................................................ 297
B. Focus group questions ..................................................................................................... 304 B.1 Student version .................................................................................................... 304 B.2 Staff version ........................................................................................................ 305
References ............................................................................................................... 307
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning vii
List of Figures
Figure 2.1: Bioecological Model of Human Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) mapped against key components of the ontology of this doctoral dissertation. ........................... 33
Figure 2.2: Research paradigm of this doctoral dissertation. .................................... 39
Figure 2.3: Activity-Centred Analysis and Design (ACAD) analytical framework, adapted from Goodyear and Carvalho, (2014b, p.59). ............. 51
Figure 2.4: Re-interpreted Activity-Centred Analysis and Design framework, adapted from Goodyear and Carvalho (2014a)............................................ 53
Figure 2.5: Model of affordance (Czaplinski, 2013, p. 105)..................................... 58
Figure 3.1: Research paradigm of the study.............................................................. 99
Figure 3.2: Sequential explanatory mixed strategy (quantitative first) with integrated qualitative instruments. ............................................................. 107
Figure 4.1: Presupposed architecture of learning networks..................................... 136
Figure 4.2: Student attendance at timetabled activities in Unit E (n=75) and in Unit S (n=60). ............................................................................................ 146
Figure 4.3: Self-reported time spent on studying, in hours. Unit E (n=69)............. 190
Figure 4.4: Self-reported time spent on studying, in hours. Unit S (n=56)............. 191
Figure 4.5: Reported number of hours per week respondents should spend on studying, Unit E, (n=74). ........................................................................... 191
Figure 4.6: Reported number of hours per week respondents should spend on studying. Unit S (n=59).............................................................................. 192
Figure 4.7: Summary of students’ attendance at all three types of timetabled activities in Unit E (n=75) and Unit S (n=60). .......................................... 195
Figure 6.1: Ecological curriculum and learning design framework of connected epistemic domains...................................................................................... 272
viii An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
List of Tables
Table 1.1: Research objectives and corresponding research questions. ...................... 4
Table 2.1: Phases and sub-processes of self-regulation according to Zimmerman (2002). ..................................................................................... 86
Table 3.1: Rationale for classification of this research as a theory-seeking educational case study. Adapted from Bassey (1999, p. 20). .................... 102
Table 3.2: Relationship between research questions, research functions and research methods........................................................................................ 108
Table 3.3: Demographic data on gender and international background, Unit E and Unit S................................................................................................... 110
Table 3.4: Unit E course enrolment, semesters 1 and 2, 2017. ................................ 110
Table 3.5: Unit S: demographic data on students’ course enrolment, semester 1, 2017............................................................................................................ 111
Table 3.6: Demographic data on highest level of qualification in semesters 1 and 2, 2017, Unit E and Unit S. ................................................................. 112
Table 3.7: Example of questions in the structured questionnaire (student version)....................................................................................................... 116
Table 3.8: Example of questions in the structured questionnaire (academic version)....................................................................................................... 117
Table 3.9: Focus group questions for students and academic teaching teams......... 120
Table 4.1: Summary of constituting elements of presupposed learning networks. .................................................................................................... 136
Table 4.2: Identified affordances in all timetabled activities and the opportunities for action they offer. ............................................................ 144
Table 4.3: Summary of identified median values, interquartile ranges and pvalues of importance and intensity of use of the lectures in both units. .... 147
Table 4.4: Summary of identified median values, interquartile ranges and pvalues of importance and intensity of use of the workshops in both units. ........................................................................................................... 148
Table 4.5: Summary of identified median values, interquartile ranges and pvalues of importance and intensity of use of the computer labs in Unit S only. ........................................................................................................ 149
Table 4.6: Types of learning affordances offered by lectures mapped against type of affordance and mode of intended interaction, both unit ................ 151
Table 4.7: Types of learning affordances offered by workshops (both units) and computer laboratories (Unit S only), mapped against type of affordance and intended interaction. .......................................................... 152
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning ix
Table 4.8: Types of learning affordances offered by computer laboratories in Unit S (only), mapped against type of affordance and intended interaction. ................................................................................................. 153
Table 4.9: Identified categories of social agents, types of learning networks and intended interactions. ................................................................................. 158
Table 4.10: Summary of identified importance, frequency, sequence and intensity of uptake of affordances for learning offered by social agents in both units................................................................................................ 160
Table 4.11: The sequence of contacting social agents for assistance with learning outside contact hours in both units mapped against the type of intended interaction.................................................................................... 162
Table 4.12: Summary of respondents’ perceptions of which electronic devices were assisting them with learning, both units. Multiple responses were allowed....................................................................................................... 168
Table 4.13: Identified online learning tools mapped against types of learning networks, intended level of autonomy and intended type of interaction. ................................................................................................. 173
Table 4.14: Summary of identified importance, frequency, sequence and intensity of uptake of affordances for learning offered by social agents in both units................................................................................................ 176
Table 4.15: Order of use, perception of the online tools, their importance, frequency and intensity of use mapped against intended level of autonomy and the type of learning network they belong to....................... 178
Table 4.16: Types of handling the artifacts mapped against required levels of self-regulation. ........................................................................................... 185
Table 4.17: Comparative analysis of respondents’ answers, number of responses and required levels of self-regulation........................................ 187
Table 4.18: Importance for students of affordances offered by timetabled activities, mapped against intended interactions, as perceived by academic teaching staff.............................................................................. 213
Table 4.19: Comparative analysis of uptake of learning affordances provided by lectures – staff and students. ................................................................. 215
Table 4.20: Comparative analysis of uptake of learning affordances provided by workshops – staff and students. ............................................................ 216
Table.4.21: Comparative analysis of uptake of learning affordances provided by computer laboratories – staff and students............................................ 216
Table 4.22: Comparative analysis of uptake of learning affordances provided by social agents – staff and students. ......................................................... 220
Table 4.23: Comparative analysis of uptake of learning affordances provided by electronic devices – staff and students.................................................. 223
Table 4.24: Comparative analysis of uptake of learning affordances provided by online learning tools – staff and students.............................................. 226
x An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
List of Abbreviations and Acronyms
ACAD framework Activity-Centred Analysis and Design frameworkEST Ecological Systems TheoryHE Higher EducationICTs Information Communication Technologies LMS Blackboard Learning Management System BlackboardLN Learning NetworkMOOC Massive Open Online Coursen = Sample size (total number of research participants in the study NL Networked LearningPLE(s) Personal Learning Environment(s)PLN(s) Personal Learning Network(s)PST(s) Problem Solving Task(s)STEM Science, Technology, Engineering, and MathematicsUCL University College LondonUS United States
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning xi
Statement of Original Authorship
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the best
of my knowledge and belief, the thesis contains no material previously published or
written by another person except where due reference is made.
Signature:
Date: 18/06/2020
QUT Verified Signature
xii An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
Publications and presentations during the candidature
1.1.1 Journal articles
Czaplinski, I. & Fielding, A. (2020). Developing a Contextualised Blended Learning Framework toEnhance Medical Physics Student Learning and Engagement. Physica Medica: EuropeanJournal of Medical Physics. https://doi.org/10.1016/j.ejmp.2020.03.010
Czaplinski, I, Turner, I, D., Helmstedt, K., Corry, P. & Mallet, D. (2019). Industry-based, transdisciplinary, complex problems as realistic settings for applying the M in STEM. International Journal of Mathematical Education in Science and Technology. https://doi.org/10.1080/0020739X.2019.1692932
1.1.2 Refereed conference papers
Czaplinski, I., Mallet, D., & Huijser, H. (2019). Addressing discrepancies between assumed, expected and actual levels of mathematical competencies: a learning design model of networked partnerships. In Proceedings of the ACSME (The Australian Conference on Science and Mathematics Education), The University of Sydney and The University of Technology, Sydney, 2-4 October 2019, Sydney, NSW.
Winter, A., Czaplinski, I., Mallet, D. & Apps, M. (2018). Fostering connectedness in WIL: Reflections on improving students’ WIL experiences, enhancing connectivity, and developing an inclusive WIL network. Paper presented at ACEN (Australian Collaborative Education Network) Conference, 3-5 October 2018, Brisbane, Australia.
Fyfield, B. & Czaplinski, I (2017). By design - facing the academic challenges of implementingtechnology enhanced learning in higher education on example of a third- year biology unit.In H. Partridge, K. Davis, & J. Thomas. (Eds.), Me, Us, IT! Proceedings ASCILITE2017:34th International Conference on Innovation, Practice and Research in the Use ofEducational Technologies in Tertiary Education, (pp. 245-254), Toowoomba, Australia.
Czaplinski, I., Sillence, M., Parsons, S., de Laat, M., Devine, C., Phillips, M., Fyfield, B. & Boman,M. (2017). What about me? Staff perspectives on the implementation of Intensive Mode ofDelivery in an undergraduate science program. In R.G. Walker & S.B. Bedford (Eds.),Research and Development in Higher Education: Curriculum Transformation, 40 (pp 106 -116). Sydney, Australia, 27–30 June 2017.
Moroney, T., Czaplinski, I., Burrage, P., & Yang, Q. (2016) How (well) are we assisting our studentsin becoming 21st century STEM graduates? In ACSME (Australian Conference on Scienceand Mathematics Education) Proceedings: The 21st Century Science and Maths Graduate,(pp. 208-214), 28-30 September 2016, The University of Queensland, Brisbane, QLD.
Czaplinski, I. & Mallet, D. (2016) Preparing future graduates to become lifelong, expert learners: Lessons and considerations from a blended learning engineering mathematics unit. In ACSME (Australian Conference on Science and Mathematics Education) Proceedings: The 21st
Century Science and Maths Graduate, (pp. 182-187), 28-30 September 2016, The University of Queensland, Brisbane, QLD.
Czaplinski, I., Mallet, D., Burrage, P. & Psaltis, S. (2015) Preparing engineering graduates for the knowledge economy through blended delivery of mathematics. In T. Thomas, E. Levin, P. Dawson, K. Fraser & R. Hadgraft (Eds.), Research and Development in Higher Education: Learning for Life and Work in a Complex World, 38 (pp 107-116). Melbourne, Australia. 6 -9 July 2015.
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning xiii
Czaplinski, I. (2015) Investigating the effectiveness of an ecological approach to learning design in a first year mathematics for engineering unit. In Proceedings of the ASCILITE2015: 32nd International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education: Globally Connected, Digitally Enabled (pp. 65-76).
Czaplinski, I., Senadji, B., Adie, L.E., & Beutel, D. A. (2014). Analysis of moderation practices in alarge STEM-focused faculty. In Proceedings of IEEE International Conference on Teaching,Assessment and Learning for Engineering., IEEE, Te Papa Tongarewa National Museum ofNew Zealand, Wellington, New Zealand, (pp. 346-350).
1.1.3 Presentations
Czaplinski, I. (2020). Industry-based, transdisciplinary, complex problems as realistic settings forapplying the M in STEM. 4th SEQ STEM Learning Development Symposium, 17 February,University of Queensland, Brisbane, Australia.
Czaplinski, I., Huijser, H., Sillence, M., & Gaede, O. (2018). Innovation and self-directed learning in the context of building and evaluating excellence: A case study in an Australian Science Faculty. Presentation based on refereed abstract. Recognising, Scaffolding and Building Excellence in University Learning and Teaching Conference, 18-19 December 2018. The University of Hong Kong, Centre for the Enhancement of Teaching and Learning.
Czaplinski, I., Mallet, D., Winter, A., Apps, M. (2018). Network-Based Framework for Providing WIL-Focused Experiences in Non-Vocational STEM Discipline: Critical Reflections. Round table discussion. ACEN (Australian Collaborative Education Network) Conference, 3-5 October 2018, Brisbane, Australia.
Czaplinski, I. (2018). The role of self-regulation and self-direction in enhancing active learning. Case study of a challenging, postgraduate STEM-focused unit. SEQ STEM Learning Development Symposium, 26 October, Griffith University, Brisbane, Australia.
Czaplinski, I., Turner, I., Mallet, D., Helmstedt, K., Winter, A., & Apps, M. (2018). Are all hands(really) on board? Re-thinking the design, delivery and evaluation of capstone units in amathematics program. Presentation based on refereed abstract. HERDSA (Higher EducationResearch and Development Society of Australasia) 2018 Conference: (Re)Valuing HigherEducation, 2-5 July 2018, Adelaide Convention Centre, SA.
Czaplinski, I. (2018). Blended learning in Medical Radiation Physics. Presentation based on refereedabstract. Cross-institutional Learning Design Forum, QUT, 14 June 2018.
Czaplinski, I. (2018). Development of a Technology Enhanced Framework for Medical PhysicsLearning and Teaching. Presentation at QUT Higher Education Research Network (HERN)Higher Degree Research (HDR) Special Interest Network (SIN), 15 March, QUT, Brisbane,Queensland.
Czaplinski, I. (2017). Designing for personalised learning: insights from a pilot of Intensive Mode ofDelivery (IMD) in a science unit. Presentation based on refereed abstract. InternationalScience of Learning Conference, 18-20 September, Brisbane Exhibition Centre, Queensland,Australia.
Fielding, A. & Czaplinski, I. (2016). Development of a Technology Enhanced Framework for MedicalPhysics Learning and Teaching. Presentation based on refereed abstract. InternationalConference on Medical Physics, 6 December Bangkok, Thailand.
Czaplinski, I., Clifford, S., Luscombe, R. & Fyfield, B. (2016). A blended learning model for firstyear science student engagement with mathematics and statistics. Presentation based onrefereed abstract. HERDSA (Higher Education Research and Development Society ofAustralasia) 2016 Conference: The Shape of Higher Education, 4-7 July 2016, CurtinUniversity, Perth, WA.
Czaplinski, I., Fielding, A. & Fyfield, B. (2016). Towards development of a blended learning model:embedding clinical medical physics education and training. Presentation based on refereedabstract. HERDSA (Higher Education Research and Development Society of Australasia)
xiv An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
2016 Conference: The Shape of Higher Education, 4-7 July 2016, Curtin University, Perth,WA.
Czaplinski, I., Burrage, P., Psaltis, S. & Mallet, D. (2014). Enhancing students’ learning through ablended learning cycle for engineering mathematics. Presentation based on refereed paper atACSME (The Australian Conference on Science and Mathematics Education) Conference,29 September – 1 October, The University of Sydney, NSW.
Sugden, S. & Czaplinski, I. (2014). Enhancing students’ conceptual understanding of mathematicsthrough the development of estimation skills with the QAMA calculator. Presentation basedon refereed paper at ACSME (The Australian Conference on Science and MathematicsEducation) Conference, 29 September – 1 October, The University of Sydney, NSW.
An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning xv
Acknowledgements
In every journey, two elements are crucial – the destination and the companionship,
and a PhD journey is no exception. Apart from learning, studying, thinking and self-
discovering on the route to becoming a scholar, I also had a great opportunity to learn
from, with and about my supervisors, excellent and highly accomplished scholars. For
this, I am extremely grateful.
My first gratitude goes to my Principal Supervisor, Professor Dann Mallet for
insightful thoughts, conscientious and critical feedback, moral support and
encouragement to complete the journey.
Next, my gratitude goes to my Associate Supervisors, Associate Professor
Denise Beutel and Dr Henk Huijser. Their critical comments, challenging suggestions
and constructive criticism helped me to develop myself as a researcher and a thinker.
I would like to express my gratitude for the invaluable work of former
supervisor, now retired, Emeritus Professor Graeme Pettet who kindly guided me
through the process, generously sharing his knowledge.
My warm thank you also goes to Professor Martin Sillence, Professor Ian
Turner, Dr Pascal Buenzli, Dr Pamela Burrage, Dr Samuel Clifford, and Dr Kate
Helmstedt, accomplished scholars and good people, who kindly agreed to undertake
research with me, sharing their wisdom, experience, thoughts and perspectives on life.
My special thank you goes to “Yoda” who I consider my friend and who always
made an effort to provide thoughtful advice.
Last but not least, I cannot thank enough my husband, Adam, for his patience,
understanding and wisdom. Thank you for being there for me. Without your warmth
and love I would never be able to complete this journey. My gratitude is never ending.
xvi An Analysis of Learning Networks of STEM Undergraduate Students To Promote Active Learning
Introduction 1
Introduction
The rapid growth of Web 2.0, a global web of socially-based tools and technologies,
the appearance of diverse digital communication platforms encompassing ubiquitous
network-based applications, and the proliferation of mobile technologies
complemented by cloud-based resources, have obliged educational institutions to
redesign their learning environments and rethink their pedagogical approaches. This
never-ending process of change has already challenged many opinions about learning
and teaching, and brought an important disruption in the ways universities understand
and fulfil their educational mission within the modern world. The modern world needs
university graduates who will become epistemically fluent knowledge workers, aware
of different knowledges and different ways of knowing (Markauskaite & Goodyear,
2017a), and who will be able to “keep on learning across and throughout their lives in
order to survive and flourish” (Jackson & Barnett, 2020, p. 14). In such a context, the
idea of assisting undergraduate students in becoming epistemically fluent, active,
lifelong learners within their own (i.e., owned by them) learning networks seems
particularly urgent (Ellis & Goodyear, 2019, p. 5).
This study has investigated the ways in which university undergraduate
students, enrolled in science and engineering degrees, were perceiving and taking up
opportunities for learning embedded in their surrounding learning environments,
composed of formal and informal learning networks. The study has also looked at
academic teaching teams’ perceptions of students’ learning practices, to propose how
learning design may be modified to better assist learners in perceiving and taking up
opportunities for learning. These opportunities, if perceived and (appropriately) taken
up, offer the potential of enabling students to become epistemically fluent, active,
lifelong active learners.
This chapter first discusses the overall problem underlying the study (section
1.1), then presents the purpose and the specific aim (sections 1.2 and 1.3), and
introduces objectives and associated research questions (section 1.4). Next, key
definitions used in the study (section 1.5) are explained. The discussion then shifts
towards the rationale (section 1.6), the background of the study (section 1.7), the study
context of Science, Technology, Engineering and Mathematics (STEM) disciplines
2 Introduction
(section 1.8), and an outline of the scope (section 1.9). The chapter closes with a
discussion of the study’s significance (section 1.10), and an overall outline of the thesis
(section 1.11), which lays the foundations for Chapter 2.
1.1 PROBLEM
Currently, new analytical frameworks and theories of learning are emerging (Boud &
Hager, 2012). These frameworks attempt to capture the changes in social, cognitive
and situated characteristics of learning processes occurring within contemporary
learning environments, which blur the boundaries between formal and informal
educational settings. Learning processes have left the brick and mortar environment to
various extents (whether this be a university learning space or the confinement of one’s
house), and have migrated into networked, virtual spaces. Such an extended and
increased complexity of learning environments and the blurred nature of their
boundaries has necessitated a careful investigation of their structures to understand, at
least to some extent, how to better use the potential for educational purposes they offer.
Research about learning networks (Goodyear et al., 2004; Goodyear &
Carvalho, 2014a; 2014b; 2016) and networked learning (Goodyear, Carvalho & Dohn,
2016) has identified the need for a broader understanding of the concept of learning,
one that encompasses formal, institutional and informal, personal learning networks
supporting connectivity and interactions (Hodgson & McConnell, 2018).
Consequently, the need to investigate the architecture of learning networks (i.e., their
formally designed and organically grown structure), and the networked learning
practices that occur within them, becomes increasingly important, as it can effectively
contribute to the research on human learning within networked environments and
inform learning design.
Learning networks, to fulfil their role as enablers of connections, need to be
productive (Goodyear, 2014; Goodyear & Carvalho, 2014a) (section 1.5 for definition)
to support co-creation of new knowledge (Goodyear & Carvalho, 2014a). Thus,
consciously perceiving opportunities for learning embedded in surrounding learning
networks, and making the decision to take them up, or not, turns out to be an important
skill in the process of becoming an active learner. Despite growing evidence provided
by educational psychology on powerful and effective learning strategies encouraging
active learning (Bjork, Dunlosky & Kornell, 2013; Dunlosky et al. 2013; Hattie &
Donoghue, 2016; Soderstrom, Yue & Bjork, 2016; Løkse et al., 2017) and facilitating
Introduction 3
deep engagement with knowledge (Marton & Säljö, 1976; Entwistle, 2000), many
misguided beliefs leading to counterproductive practices still persist amongst both
academic teachers and learners (Kornell, Rhodes, Castel, & Tauber, 2011; Roediger
& Pyc, 2012; Soderstrom & Bjork, 2015; Winne & Jamieson-Noel, 2002). Thus, there
is a need to investigate learning practices within the specific contexts of learning
networks, to identify some evidence-informed learning design solutions that would
increase learners’ awareness of diverse opportunities for learning offered by the
connected environment of learning networks (formal and informal).
This study addresses the above-defined problem by presenting an in-depth
analysis of connections and relationships between constituting elements of learning
networks, and the ways networked learning practices are applied in the context of
higher education (HE) units of study in science and engineering. There are several
benefits of such an in-depth, contextualised analysis. First, it allowed for a better
understanding of respondents’ perceptions of the architecture of formal and informal
learning networks, their constituting components, and the learning affordances they
offer. Furthermore, student respondents’ self-reported claims allowed us to identify
perceptions of their own learning practices within the learning networks. Finally, the
study also revealed academic teaching teams’ perceptions of their students’ practices
within learning networks. In summary, the study painted a comprehensive picture of
the ways student respondents thought they were using the learning affordances and the
academic teaching teams’ perceptions of the uptake of learning affordances by their
students. Such an analysis offered the potential to identify areas for improvement,
formulate conclusions, discuss implications, and suggest further research directions
(see section 6.0 for details).
1.2 PURPOSE AND AIM
The purpose of this study was to inform research into learning design leading to
ways of designing evidence-informed, context-specific, learning experiences to assist
first year Science, Technology, Engineering, and Mathematics (STEM) undergraduate
university students in becoming epistemically fluent, lifelong active learners.
4 Introduction
More specifically, the aim of the study was to gain a deeper understanding of
how learning affordances offered by presupposed1 learning networks (formal and
informal) are perceived and taken up by first year undergraduate students enrolled in
STEM-focused disciplines (science and engineering); how these affordances are
enacted by student respondents through their networked learning practices; and,
finally, how the uptake of learning opportunities and networked learning practices are
perceived by academic teams teaching student respondents.
1.3 OBJECTIVES AND RESEARCH QUESTIONS
To achieve the above-mentioned purpose and aim, the study investigated
presupposed formal, i.e., institutionally designed, and informal, that is, personal
learning networks and learning practices, of undergraduate students enrolled in first
year STEM-focused disciplines at a large, metropolitan university in Australia. To
allow deep analysis of the learning environment, and the learning practices occurring
within it, the study also investigated academic teaching staff’s perceptions of students’
learning networks and their networked learning practices within the above-identified
learning networks. To this end, three research questions, supported by corresponding
research objectives, were formulated, as presented in Table 1.1.
Table 1.1: Research objectives and corresponding research questions.
Research objective Research questionRO1: To identify which constituents ofpresupposed learning networks are beingused by STEM students enrolled in twoSTEM-focused, first year units of study.
RO2: To identify networked learningpractices occurring within the presupposedlearning networks.
RQ1: What learning affordances offered by presupposed learning networks were perceived and taken up by the undergraduate STEM students?
RQ2: What networked learning practices are occurring within the presupposed learning networks?
1 While the concept of presupposition is explained later in the thesis (section 2.8), it is to note that presupposition implies a prexisting condition. Thus, the existence of a learning network was presupposed by the researcher based on her frame of reference and functional context that was in part partially designed by the researcher, and in part organically grown. Thus, the existence of learningnetworks was a pre-conditioned/ presupposed part of learning/ educational environments.
Introduction 5
RO3: To identify academic staff’sperceptions influencing/ guiding learningdesign.
RQ3: What are academic staff participants’ perceptions of students’ networked learning practices within the presupposed learning networks?
1.4 DEFINITIONS
While the details of the definitions used in this study and the theoretical background
underpinning their meaning are presented in Chapter 2, this section collates the
definitions of key concepts, as part of introducing the background of the study.
Building on research within educational psychology (Bjork, Dunlosky &
Kornell, 2013; Dunlosky et al. 2013; Hattie & Donoghue, 2016; Løkse et al., 2017;
Neelen & Kirschner, 2017; Kornell, Rhodes, Castel, & Tauber, 2011; Roediger & Pyc,
2012; Soderstrom & Bjork, 2015) an active learner is defined as a “sophisticated and
effective learner” (Bjork, Dunlosky & Kornell, 2013, p. 314). Bjork, Dunlosky and
Kornell (2013) explain that the process of becoming an active learner requires a learner
to reach a high level of self-regulation and self-direction, and encompasses the
following five capabilities:
1. Understanding the principles of the learning process;
2. Knowing effective learning strategies, and
3. Consciously applying them depending on learning conditions;
4. Managing conditions of learning; and finally,
5. Developing capability to monitor and control one’s learning.
(p. 314).
A careful analysis of learning networks allows for identification of areas requiring
learners to develop (or improve) the above-mentioned five capabilities. In addition,
the exploration of the architecture of learning networks enables identification of
factors impacting on their effectiveness in promoting their productivity (Goodyear
2014; Carvalho & Goodyear, 2014a), and may lead to the identification of potential
solutions in the form of learning design.
With regards to self-regulated learning, this study has used the definition of
the concept developed by Schunk and Greene (2018), according to whom self-
regulated learning consists of “the ways that learners systematically activate and
6 Introduction
sustain their cognitions, motivations, behaviors, and affects, toward the attainment of
their goals” (p. 1).
As for self-directed learning, Knowles’ (1975) definition of the concept has been
used to describe the process “ in which individuals take the initiative, with or without
the help of others, in diagnosing their learning needs, formulating their own goals,
identifying human and material resources for learning, choosing and implementing
appropriate learning strategies, and evaluating learning outcomes (p. 18)”.
The next set of definitions is related to the overarching concept of learning
networks. In this study, the following definition of learning network has been coined,
based on the writings of Goodyear and Carvalho (Goodyear 2014; Goodyear &
Carvalho, 2014a; 2014b; Goodyear & Carvalho, 2016), Jones (2015) and Czaplinski
(2013). A learning network is an assemblage of 1) tools and 2) artifacts used within
the environment under investigation, 3) people (social agents), constituents of the
learning networks under investigation, 4) ideas (related to learning and teaching,
expressed and/ or implemented by social agents), and 5) practices (enacted by the
social agents). This assemblage offers learning affordances which can be perceived
or not, and taken up or not. While the perception of affordances depends on the social
agent’s frame of reference, the decision of taking up the affordance or not is triggered
by the functional context (the foundation of the need to complete a learning task) and
enacted by the social agent. Socio-constructivist perspectives on learning see the
process of learning as a phenomenon of constructing new knowledge, during which
social agents (human beings) co-construct knowledge through their interactions in/
with the environment (Vygotsky, 1930, 1978; Bruner, 1966, 1990). The process
transforms both, human beings (as they have learnt new knowledge) and the
environment, as the knowledge was applied and resulted in change, either physical or
intellectual.
From this perspective, the below presented definitions of the productive
learning network and networked learning practices have been adopted in this study2.
Learning networks that form the environment, are productive when they
support the co-construction of knowledge, the process of taking distributed
2 The rationale for advancing modified definition of productive learning networks and networked learning practices is presented in Chapter 6.
Introduction 7
knowledge from the network, de-, re- and co-constructing it, and feeding it back to
the network, in the form of an intellectual contribution (e.g. digital or material artifact,
assisting other learner with learning) and enable feeding it back to the network, in the
form of an intellectual contribution (e.g. digital or material artifact, assisting other
learner with learning).
Considering the key importance of a learning network in being productive to
facilitate the co-creation of knowledge, building on previous definitions of networked
learning discussed in Chapter 2 (e.g. Goodyear et al., 2004; Goodyear & Carvalho,
2014a; 2014b), and socio-constructivist perspectives on learning, the following
definition of productive networked learning was advanced to be used in this study:
productive networked learning is understood as the process that involves the
application of an assemblage forming a learning network (composed of artifacts,
people, ideas and practices), as part of taking action by a learner to complete a learning
task. During this process, the knowledge is de-constructed and re-constructed (see
above paragraph for definition of the process), and the learning environment within
which the activity takes place is co-configured. As a result, new knowledge is co-
created and re-distributed within the network.
Finally, in this study, a learning networks’ core is composed of the key
constituting elements and the connections between them. The connections play a
double role, they facilitate the interactions and relationships and make the boundaries
of a learning network. Inside these boundaries, a close observation of how activities
are enabled, inter-connected and inacted took place, resulting in an in-depth analysis
of learning networks and networked learning practices.
The analysis of learning networks and networked learning practices informs
the ways learning tasks are designed, with the aim of improving learning experiences,
developing teachers’ and learners’ ways of knowing conducive to epistemic fluency,
and informing research within learning design. For this reason, the next two definitions
explain the concept of epistemic fluency and learning design, as they are understood
in this study.
Following on from Markauskaite and Goodyear (2017a; 2017b; 2018a; 2018b),
the concept of epistemic fluency refers to “a deep understanding of how knowledge
works, the capacity to participate in the creation of actionable knowledge and a sense
of how to reconfigure the world in order to see what matters more clearly and enable
oneself, and others, to act more knowledgably” (2017a, p. 20). This definition
8 Introduction
emphasises the constructivist nature of learning, makes salient the productive character
of learning networks, and highlights the actionability of knowledge, the condition of
epistemic fluency, especially for future knowledge workers (Markauskaite &
Goodyear, 2017a). However, epistemic fluency, as any other higher order capability,
needs to be developed. From the perspective of this study, one of the ways of assisting
undergraduate learners in becoming epistemically fluent, active, lifelong learners, is
enacted by learning design.
This study uses the definition of learning design, as developed by Mor, Mellar,
Warburton and Winters (2014), and reported on by Hansen and Dohn (2018). A
learning design is “an educational pattern that supports specific actions in typical
situations and, in compact form, collects the central part of a practice that can be
communicated to others” (p. 448). That is, the concept of learning design encompasses
theoretical and practical components that are complementary in relation to each other.
Firstly, patterns are related to the theoretical, conceptual aspects of learning design
(i.e. theories of learning, pedagogical approaches), which is then followed by a
practical aspect, that is, a generalised account of teaching practice that can be shared
with the educational community and, potentially, replicated/ enacted within specific
educational contexts.
1.5 RATIONALE FOR THE STUDY
This section briefly discusses the calls for more research on contextualised learning
designs. These consistent calls come from five different perspectives: research on
learning networks and networked learning, epistemic fluency, STEM education, active
learning, and learning design. All five perspectives are outlined below.
1.5.1 Learning networks and networked learning
The idea that technology, by enabling diverse types of connections and facilitating
interactions (see section 2.2.1 for details), is the vehicle for making the learning
process accessible and collaborative is not new. From the beginning of the research
into learning networks, the key concept of co-creation of knowledge through
interaction, to promote different ways of knowing and epistemic fluency, has been
present (Markauskaite & Goodyear, 2017a).
The origins of networked learning can be traced back to at least the 1970s
(Goodyear et al., 2015). Ivan Illich, philosopher, thinker, and educator saw new
Introduction 9
technologies as a vehicle for free and egalitarian access to learning, which is
considered one of the precursors of networked learning. In 1971 Illich published
Deschooling society (Illich, 1971), a book that, by advocating for homeschooling, laid
some of the foundations for the idea of networked learning, including the nature, the
purpose and the way of operationalising a learning network. Illich (1971) saw four sets
of resources that facilitate learning through collaboration within a network: peers,
more advanced peers, artifacts, and models “that any learner should freely access in
the process of defining and achieving one’s own educational goal” (Illich, 1971, p.
76). The access should be provided through “opportunity webs”, “learning webs”, or
“networks, freely available to all and designed to spread equal opportunity for learning
and teaching” (Illich, 1971, p. 77).
Illich’s ideas about the nature of networked learning, although almost a half-
century old, are still valid, especially in the context of research on modern, digital age
learning (Davidson & Goldberg, 2010) and learning environments (Carvalho &
Goodyear, 2014a, 2014b; Carvalho, Goodyear & de Laat, 2017; Carvalho & Goodyear,
2018). In particular, the idea of co-creating knowledge through a collaborative process
of learning with equal or more advanced peers through “opportunity webs” resonates
with constructivist theories of learning, especially with Vygotsky’s socio-cultural
theory (1978)3 and Engeström’s activity theory (Engeström, 2000; 2015). Seeing
learning as a collaborative co-construction of knowledge within a universal (global)
learning network, resulting from free interactions between entities of the network, was
certainly a very progressive vision of education. Moreover, it appears that from the
beginning, Illich’s (1971) ideas stemmed from criticism of formal, institutional,
education systems, by emphasising the educational potential of learning networks, and
thereby blurred the boundaries between formal, institutional, designed learning
networks and informal, personal, self-made, and organically grown personal learning
networks. Modern digital learning, or participatory learning (Davidson & Goldberg,
2010), enhances the blurriness of the boundaries and highlights the importance of
learners’ agency in making and managing their learning networks. This type of
learning happens on an everyday basis through a plethora of enabling technologies,
the most common being social media sites such as blogs, wikis, discussion fora,
3 Vygotsky’s “Mind in society” was published in Russian in 1930, and translated and published in English in 1978.
10 Introduction
Facebook, and Twitter, to name only a few. Davidson and Goldberg (2010) observe
that this richness of collaborative possibilities is the key characteristic of digital
learning:
Digital learning is not simply about interaction (we all have plenty
of that in our lives) but of interaction that, because of issues of
access, involves co-creation with myriad strangers who have the
anonymity to respond as they wish, candidly. (p. 5)
This has important consequences for learning design, as the questions of “how to
design formal learning networks?” and “how do learners make their own personal,
informal learning networks?” are directly followed by the question of “how to assist
contemporary learners to realise the potential offered by both formal and informal
learning networks to provide for learners’ development, first as learners and next as
professionals?” In short, how to assist learners in becoming self-regulated, self-
directed, agentic managers of their own learning?
1.5.2 Epistemically fluent lifelong learners
In the age of the digital economy (Bates, 2015; De Laat & Dawson, 2017; Griffin &
Care, 2015), the challenge of assisting students in becoming active, lifelong learners
is of strategic importance for economic growth and progress.
With the shift in the nature of the economy from being industry-based to
knowledge-based (Powell & Snellman, 2004), educational institutions have a
responsibility to respond to “the need to develop practices that actively promote
twenty-first century literacies or capacities” (De Laat & Dawson, 2017, p. 164). These
practices are also referred to in the literature as 21st century skills and capabilities4
(Bowman, 2010; Griffin & Care, 2015; McGaw 2013; Lamb, Maire & Doecke, 2017)
or graduate attributes, in the context of higher education (HE) (Bowman, 2010). For
HE institutions, this implies an important and urgent need to foster students’
development as lifelong, active learners of discipline knowledge, and its application
4 For a critical discussion of the term 21st century skills, see Neelan and Kirschner (2016) and Kirschner and Stoyanov (2018). Although agreeing with those researchers who argue for more precise use of the term “21st century skills”, for consistency reasons, this study will still use this expression to describe an overarching umbrella-term of skills and capabilities that encompasses skills, knowledge, work habits, character traits, and digital literacy (Neelan & Kirschner, 2016).
Introduction 11
within contemporary complex professional environments. However, research
(Markauskaite & Goodyear, 2017a) indicates that this constitutes a considerable
challenge for all educational institutions, which often fail in achieving that part of their
mission, especially with regards to the need to educate future knowledge workers. As
Markauskaite and Goodyear (2017a), observe:
[T]he nature of professional work is changing quite rapidly, and
there are signs that some of the established ways of distributing
professional education between the university and the workplace are
in crisis. Employers and their friends in government express deep
concerns about the capacity of universities to prepare graduates who
are ready for work. (p. 3)
As opposed to many researchers who focus on the concepts of skills and capabilities
(ATC21S Project, n. d.; Bowman, 2010; Griffin & Care, 2015; McGaw 2013;
FLIPCurric, n. d.), Markauskaite and Goodyear (2017a; 2017b; 2018a; 2018b) take a
more holistic view and analyse the question of educating lifelong, active learners from
the perspective of the required type(s) of knowledge(s) contemporary graduates need
to acquire, and the fluency of using this knowledge/ these knowledges in complex
workplaces. Interested in the concept of knowledge, Markauskaite and Goodyear,
(2017a) aim to “understand how knowledge works in routine and innovative
professional activities” (p. 1), as contemporary workplaces are becoming increasingly
complex with trans-and multi-disciplinary teams working together to solve complex
problems. Working and collaborating within such environments requires employees to
apply diverse types of “specialised and context-dependent knowledges” and “different
ways of knowing” developed during their study to solve a given problem
(Markauskaite & Goodyear, 2017a, p. 1). Contemporary employees, to be able to work
within strongly knowledge-focused professional environments, need to be self-
regulated and self-directed knowledge workers (Markauskaite & Goodyear, 2017a).
However, HE institutions focus predominantly on developing students’ epistemic
cognition, i.e. understanding about how knowledge is created (constructed), while
epistemic fluency receives less attention. The authors argue that, in times of increasing
complexity and growing numbers of wicked problems our society faces on an everyday
basis, universities have an obligation to “provide high-quality professional education”,
12 Introduction
to ensure the society can “place trust in universities” (Markauskaite & Goodyear,
2017a, p. 3) to address some of these problems. From the perspective of this study,
making (productive) connections and interacting within available (learning) networks
of connections is also a way of knowing that can be used (fluently) to co-construct new
knowledges. Moreover, in a contemporary world, the capability of transgressing
diverse domains of knowledge(s) and applying different ways of knowing is a primary
capability, encompassing both epistemic cognition and epistemic fluency.
Markauskaite and Goodyear (2017a) point towards the realised and urgent need
of academics to foster students’ epistemic fluency. The problem is that the academic
content experts do not always have the knowledge, experience and/or tools to enable
students’ learning of diverse knowledges. Evidence-informed (Neelan & Kirschner,
2018b), and/or evidence-based (Markauskaite & Goodyear, 2017a) practice within
learning and teaching should focus on designing overall learning experiences,
including learning environments and learning tasks, that “prompt students to activate
those epistemological resources that are productive in particular situations, rather than
focusing on valuing and fostering particular types of beliefs (e.g. only the sophisticated
ones)” (p. 177).
This study attempts to respond to the above-formulated need, by conducting
the research from two perspectives – learners and academic teachers. Although all
three research questions investigated respondents’ perceptions of the ways students
were using the potential for learning offered by formal and informal learning networks,
and the perceptions of students’ networked learning practices, the findings resulting
from the research questions led to recurring patterns. These patterns, constructed on
the basis of self-reported claims from two types of respondents, and thus (to some
extent) complementary, may be used as roadmaps containing signposts for rethinking
learning design to promote self-regulated and self-directed active learning, a sine qua
non of epistemic fluency.
This has enabled the purpose (section 1.2) and the aim (section 1.3) of the
study, and has ultimately led to the emergence of a contextualised (i.e., located in the
STEM disciplines) and a situated (within specific university units of study) evidence-
informed ecological curriculum and learning design framework of connected
epistemic domains (see Chapter 6 for details).
Introduction 13
1.5.3 Strategic importance of STEM education
The importance of STEM education has been internationally recognised and discussed
for quite some time now. For instance, in the United States of America (USA), the
Committee on STEM Education of the National Science and Technology Council
(NSTC CoSTEM Report, 2018) has emphasised that “America’s national innovation
base depends more than ever on a strong, cross-sector collaboration around common
STEM education interests and goals – a STEM ecosystem – that can provide all
Americans with access to high-quality STEM education throughout their lifetimes”
(p.1).
The role of STEM education as a key factor contributing to the social progress
and economic growth of the Asia Pacific region has also been acknowledged, and
voices calling for effective teaching of STEM disciplines have been heard (The New
York Academy of Sciences, 2017). In Europe, the European Union Commissioner for
Education, Culture, Youth and Sport describes STEM education as a crucial element
in dealing with social challenges (CSR Europe, 2016). The Commissioner points to
the necessity of tightening collaboration between educators and employers to
investigate, understand, and develop effective strategies to equip students in facing the
above-mentioned challenges.
The Australian Government has also identified STEM education as a strategic
priority across all sectors: government, industry, and education, including HE (ATSE,
2016; Chubb, 2013; Finkel, 2016; Norton & Cakitaki, 2016; Prince, 2016). STEM
education “prepares a skilled and dynamic STEM workforce and sets the foundations
for lifelong STEM literacy in the community, shaping perceptions of the role of STEM
in society” (Office of the Chief Scientist, 2014, p. 2). Numerous and diverse reports
and documents have been written that address this national issue of the highest,
strategic priority (Finkel, 2016). All documents emphasise that HE carries the
responsibility for educating the future epistemically cognisant and fluent workforce.
That means that HE institutions are required to equip graduates with appropriate tools
to enable them to develop the skills and capabilities of working independently and in
collaboration, be self-regulated, self-directed, active, and epistemically fluent
members of the teams.
This study responds to this need, as its setting consists of STEM-focused
disciplines such as engineering and science. Furthermore, this study contributes to the
body of knowledge about ways to assist university graduates in becoming knowledge
14 Introduction
workers within the STEM-focused disciplinary fields that are, as discussed above, a
high priority for governments and industry around the globe.
1.5.4 Active learning
Research (Bjork, Dunlosky & Kornell, 2013; Koriat & Bjork, 2005; Soderstrom, Yue
& Bjork, 2016) has demonstrated that many people “often have a faulty mental model
of how they learn and remember” (Bjork, Dunlosky & Kornell, 2013, p. 417). This
leads to mis-assessment of one’s own effectiveness in the learning process, and mis-
management of learning activities. In turn, such mis-assessment and mis-management
often result in applying trial and error techniques to learning that give a short-term
learning performance that may lead to temporary impression of effectiveness. If
applied in the longer term, without any self-reflection or appropriate self-assessment,
learners mislead themselves and develop an inaccurate impression of effectiveness and
achievement of their learning (Winne & Jamieson-Noel, 2002; Kornell, Rhodes,
Castel, & Tauber, 2011; Roediger & Pyc, 2012; Soderstrom & Bjork, 2015). This
phenomenon, called an illusion of competence (Koriat & Bjork, 2005; Soderstrom,
Yue & Bjork, 2016), can be described as “the conditions that give rise to an
overestimation of one’s future memory performance” (Koriat & Bjork, 2005, p. 187).
This, in turn, may prevent learners from becoming effective and sophisticated in their
learning practices, that is, from becoming active learners.
However, to be effective, a learning strategy leading to active learning needs to
be contextualised and situated within a relevant learning environment and evidence-
based or evidence-informed. Evidence-informed means that proposed solutions, such
as the learning design model that emerged as a result of this study (section 5.4.2), are
“using evidence from scientific research” (Neelan & Kirschner, 2018b, n. p.);
however, as opposed to evidence-based, they have not been tested yet for efficacy in
practical (or clinical) trials. Educational research needs to consider a large number of
variables, which might produce the impression of a lack of rigour or even
unpredictability, which then strongly impacts on the (perceived) effectiveness of the
solution. As Neelen and Kirschner (2018b) observe “when we use evidence, we need
to acknowledge that what works in one context doesn’t necessarily work in another”
(n. p.). For this reason, research reporting on educational research conducted within
the realistic settings of the learning environment (e.g., classroom, learning network of
a university unit of study) and informing learning design are so important.
Introduction 15
1.5.5 Learning design
The emerging discipline of learning design originated from research into Information-
Communication Technologies (ICTs), and online learning environments. The
advances in these fields challenged the roles and relationships between learners,
teachers and the environment, and understandings of the learning process (Konnerup,
Ryberg & Sørensen, 2018). Learning design plays a unique role in modern society – it
informs the ways learning experiences are designed to best respond to the quickly
changing educational needs of contemporary society. For quite some time now
researchers in learning design have been struggling to develop a common descriptive
framework for teaching and learning activities, one that would provide a sufficient
amount of information to allow successful replication of the design (Danziel, 2015).
Such a descriptive framework, by encompassing all constituents of the learning design
process, would promote adoption of new approaches to learning and teaching and
foster sharing of this experience. On the other hand, Mor, Mellar, Warburton and
Winters (2014) report on the real and urgent need to “find effective ways of sharing
design knowledge, particularly if practitioners are to make any headway in building
on the success of others in a cumulative manner” (p. 1). More recently, Carvalho and
Goodyear (2018) have called for more research into learning design, especially in the
context of service design.
In summary, this section has provided systematic evidence of a real need for
investigating learning design that enables active learning and assists students of
STEM-focused disciplines in becoming epistemically fluent, active, lifelong learners.
This contextualised and situated study is a response to the above-identified needs.
1.6 SCOPE OF THE STUDY
As a result of the scoping activities described in rationale for the study (section 1.5),
the study was narrowed down to an investigation of:
1. Learning networks and networked learning practices of students enrolled in
first-year science and engineering degrees at a large metropolitan university
in Australia.
2. Academic teaching staff’s perceptions of students’ perceptions and uptake
of opportunities for learning offered by the learning networks (formal and
informal).
16 Introduction
By narrowing down the breadth, the research focused on a highly detailed description
of this embedded case study. That is, the study was conducted with two mathematics
units of study offered to science and engineering students at a large metropolitan
Australian university. The mixed research methods included paper-based surveys from
135 students and eight academic teachers, as well as six focus groups with students
and three focus groups with academics teachers. The details of the research
methodology data collection techniques and in-depth data analysis are discussed in
Chapter 3.
1.7 SIGNIFICANCE OF THE STUDY
This section discusses the contribution this study makes to research on learning
networks and networked learning practices, investigations of factors that impact on
students’ active learning, and advances in the field of learning design.
The findings of the study shed light on first year university undergraduate
students’ perceptions of the learning affordances embedded within their surrounding
environment, understood as learning networks (formal and informal). Furthermore, the
analysis also provides insights into self-reported networked learning practices.
Combined, these findings contribute to a better understanding of how first year
university undergraduate students, enrolled in STEM-focused disciplines, are
appropriating surrounding learning environments (places, spaces), and adjusting their
learning practices to new environments. The study shows challenges that students
faced in terms of their awareness of effective learning strategies, and skills in using
these strategies efficiently. This is an important contribution to contextualised learning
design in the context of an example of first year, undergraduate, STEM-focused units
of study, as it helps to identify critical areas in designing learning environments and
raising students’ awareness of how to use such environments effectively for their
learning. Thus, the study offers the potential to inform curriculum and learning design
by pointing towards specific challenges related to student uptake of learning
affordances, as related to the development of their self-regulated and self-directed
learning skills.
The study further compared student perceptions with academic teaching teams’
perceptions, which allowed for the identification of certain discrepancies in the ways
both cohorts saw the potential for learning offered by learning environments. Thus,
this comparative study effectively contributes to curriculum and learning design by
Introduction 17
pointing towards a vital need for closer collaboration between academic teaching
teams and experts in the field of curriculum/ learning design.
Finally, the study contributes to its underpinning ontological and
epistemological frameworks. That is, the concept of affordance, as used in the study,
has been modified, along with the concept of productivity of learning networks.
Furthermore, the concept of interference of perceptions is proposed to describe the
pattern of divergent and convergent perceptions of students and academic teaching
teams. Moreover, the study proposes to redefine the position of the academic teacher
as a (content) expert in the network.
The study culminates with a proposed ecological curriculum and learning
design framework of connected epistemic domains that have emerged from it. The
proposed framework acknowledges the fact that there are different types of
knowledges (held by different partners/ stakeholders), different levels of expertise
represented by diverse partners/ stakeholders, and that curriculum/ learning design
necessitates a harmonious collaboration between different experts to design tasks that
offer the potential of assisting students in becoming active, epistemically fluent,
lifelong learners. Thus, to assist first year undergraduate students of STEM-focused
disciplines to become epistemically fluent, active, lifelong learners, the ecology of
curriculum/ learning design needs to be transformed to allow collaboration founded on
the principle of partnerships of diverse stakeholders.
This proposed ecological framework also offers a more holistic (and
humanistic) dimension. The study aspires to provide a contribution to the research on
learning ecologies and ecologies of practice, increasingly important topics in
educational research (Ellis & Goodyear, 2019; Jackson & Barnett, 2020). As Jackson
and Barnett (2020) observe in their recent book on ecologies for learning and practice:
The ideas of learning ecologies and ecologies of practice – within
which ecologies for learning are embedded – carry hope: hope that
by persons, communities, organisations, and societies learning about
themselves and caring for the consequences of their actions, the
world might sustain itself. And connected to this hope is value: the
ecologies we create to achieve something are the means by which
we develop meaning. (p. 23)
18 Introduction
As a researcher and author of this doctoral thesis, I hope that this study will contribute
to an understanding of how the distributed knowledge is used within contemporary
environments and how this understanding can be used for the benefit of learners.
1.8 BACKGROUND TO THE STUDY
The initial reason for undertaking this study was out of concern for two groups of
university stakeholders. The first group consists of undergraduate, first year
undergraduate students, enrolled in STEM-focused disciplines. My role as a researcher
within education, with a particular interest in learning design, involved designing and
developing learning experiences, often offered in blended learning mode, for the
above-mentioned group of students. Another aspect included evaluating the
effectiveness of the proposed learning experiences, especially from the perspective of
student engagement with the online resources provided in the Learning Management
System (Blackboard), and the level of achievement of related learning outcomes, in
terms of assessment results and progression. Finally, to complement the picture, my
professional activities also involved investigation of student satisfaction levels. Over
the years, a systematic analysis of evaluations of the students’ achievement of learning
outcomes, engagement with the unit (e.g., attendance) and the usage of the available
tools (e.g., LMS analytics) have provided evidence to me that many students do not
fully realise the potential for learning embedded in the designed and contextualised
experiences. It was also evident to me that many of the students do not know how to
approach specific learning tasks, what to do with certain tasks, or are confused
regarding the effective learning strategies to apply within this environment. As a result,
many students struggle, especially with understanding and applying concepts in
practical, authentic situations. This experience thus helped me to identify the recurring,
important, and challenging problems that were then crystallised in the form of three
research questions of this study.
Learning design involves a close collaboration with academic educators/
academic teaching teams, who design and develop learning experiences and
implement them in their teaching. During conversations with academic educators, I
often heard frustration caused by a (perceived) lack of student engagement. Yet, a great
effort was put into the learning design phase by academic educators, with expectations
(and hopes) of improved students’ engagement manifested by improved attendance at
lectures, active participation in tutorials and workshops, and completion of online
Introduction 19
activities (not assessed). However, quite often such effort resulted in (perceived) low
student engagement inciting academics to over-assess students to force engagement.
Through the above-mentioned observations, reflections and discussions, it
became evident that academic educators would also benefit from an in-depth, research-
underpinned analysis of students’ engagement with knowledge within both formal and
informal learning networks. To scope the study, an initial literature review was
conducted and consultations held with key stakeholders involved in the process of
learning design (see section 5.4.2 for description):
1. Academic content experts designing learning experiences and teaching them
in STEM-focused curricula;
2. Educational experts in approaches to learning and teaching methodology,
working in close collaboration with academic experts (e.g., curriculum
designers, learning designers);
3. Educational expert researchers involved in higher education research;
4. University partners supporting academic content experts;
5. Student partners.
The conclusions drawn from this activity not only confirmed the need for undertaking
this research, but also allowed me to scope the study and to identify active learning
(Bjork, Dunlosky & Kornell, 2013), learning networks and networked learning
practices (Carvalho & Goodyear, 2014a, 2014b; Goodyear & Carvalho, 2016) as the
research focus. The scoping was complemented by overall research activities of the
author of this thesis. The need for investigating the process of becoming an active
learner has been reported in several collaborative publications (Czaplinski, 2015;
Czaplinski & Mallet, 2016; 2019; Czaplinski et al., 2015; Moroney et al., 2016;
Czaplinski et al., 2017; Czaplinski & Mallet, 2019; Czaplinski, Mallet & Huijser,
2019; Czaplinski & Fileding, 2020).
1.9 CONTEXT OF THE STUDY
Two large-enrolment, first year undergraduate units of study, one in an engineering
degree and one in a science degree at the university in focus, were selected for
conducting the research. There were interesting similarities between the two units,
which presented important challenges, especially from the perspective of curriculum
20 Introduction
design (degree level) and learning design (unit level). For instance, both units were
compulsory and focused on foundational quantitative skills for their respective
degrees, meaning they provided essential content knowledge, and both were taught by
academics from the School of Mathematical Sciences. Both units implemented many
initiatives (from improving blended learning pedagogy to introducing online self-
diagnostic tests for enrolled students), aimed at assisting students with both learning
mathematical topics and fostering development of their self-regulated and self-directed
learning. Finally, both units had large numbers of enrolments, which impacted on the
approaches to learning and teaching, assessment design, and organisation of the units
(e.g., timetables with multiple tutorials, workshops, many casual teaching staff). The
similarities and challenges of both units are classified in two categories:
1. Requirements of the course of study (e.g. Bachelor of Engineering or
Science):
a. Providing foundational knowledge in mathematics to ensure the
fundamentals of mathematical knowledge were offered to all students;
b. Challenges posed by the choice of pedagogy (predominantly
experiential learning and blended learning approaches) used in both
units;
c. The assessment regime (emphasis on authentic assessment, number of
assessment items, increased importance of formative feedback);
d. Challenges associated with large enrolment numbers (i.e. managing
large cohorts, managing large numbers of casual staff). 2. Challenges associated with student profile:
a. Diversity of students’ educational backgrounds;
b. Students’ preparedness levels in mathematics;
c. Diversity of degrees students were enrolled in (hence diverse learning
needs);
d. Challenges associated with “transitioning in” to the university;
e. Students’ (low level of) awareness (and knowledge) of effective and
powerful learning strategies to be used in the learning environment of
the units of study.
Introduction 21
As such, the units of study offered an opportunity for analysing authentic
problems faced by STEM academics and students at many universities, increasing the
applicability of the findings and positively impacting on the contribution to the body
of knowledge made by this study.
As the functional context plays an important role in data analysis and
interpretation, the functional contexts of both units are discussed in detail below.
1.9.1 Functional context of Unit E
Unit E (Engineering), a first year unit offered in both semesters, introduced students
to the fundamental concepts in mathematics necessary for engineering disciplines.
Apart from this, the unit also provided those students who were not fully
mathematically prepared with the opportunity of catching up on their mathematical
knowledge. In this way, the unit contributed to facilitating students’ transition to the
university. The next section discusses the functional context (i.e., the foundation of the
need to complete a learning task) of the unit in depth.
The unit’s learning outcomes emphasised a focus on the provision of
mathematical foundations for engineering students and was strongly contextualised,
focusing on applicability of studied mathematical concepts in engineering situations.
A blended learning approach was applied as the unit’s teaching mode, with diverse
pedagogical methods “thoughtfully integrated” (Alammary, Sheard & Carbone, 2014,
p. 443) to enhance students’ learning experiences. The unit used a learning flow (i.e.,
pedagogical cycle) of content presentation and practice during the on-campus, face-to-
face sessions, followed by online tasks designed to encourage retrieval of information
to be applied in a new context (e.g., online quizzes), and in this way, it aimed to
promote the development of students’ self-regulated learning. Furthermore, the unit
provided a rich offer of online learning tools to students, presenting them with the
opportunity to start developing their self-directed learning.
During the on-campus teaching phase, the unit used two types of weekly,
timetabled activities: two hours of lectorials (large group, interactive lectures),
providing the theoretical mathematical content embedded in an engineering context,
and two hours of small group workshops, focused on application of the theory and
development of skills. The content presented during lectorials was practiced during
workshops, using team-based collaborative approaches to complete designed tasks.
22 Introduction
The on-campus, timetabled activities were complemented by the unit’s online
learning platform. The Unit Coordinator used the Learning Management System
(LMS) Blackboard to communicate with students, supply all required information
(timetable, assessment schedule and criteria, etc.), and provide online learning tools,
including resources and links to external online sites. The platform also facilitated
completion of self-paced activities, for instance online quizzes. In addition, the unit
used a Facebook page, set up and maintained by students, and opened to the teaching
team who contributed to the site from time to time and monitored academic integrity
by ensuring students were assisting each other in their learning (e.g., by sharing advice,
explaining problems, sharing/ providing resources, giving suggestions on how to solve
problems) as opposed to providing ready responses.
To strengthen the learning flow, that is the logical connection between on-
campus and online components of the unit, two types of learning activities were
introduced. First, at the end of each weekly lectorial, students were presented with a
challenge question, a highly contextualised mathematical problem related to the
freshly covered topic. The concept of challenge question also served as a factor
reinforcing the relevance of the unit’s content with regards to students’ chosen degrees
and provided a form of encouragement to students to attend the lectorials. A second
learning activity consisted of flipping the learning by providing problem sheets online
(and prior to the workshop) to be attempted during workshops. This approach had two
purposes. First, it provided students with the opportunity of practicing before the
workshop, preparing them to take up the learning affordance of asking the teaching
team specific questions. The second aim was to promote self-regulated learning (e.g.,
planning for the study week, encouraging studying routines) and self-directed learning
(e.g., students’ self-diagnosis of their learning needs and the ways of satisfying them).
The assessment design followed spaced practice principles with assessment
items spread throughout the semester. Besides the already mentioned online quizzes,
the assessment also included two problem-solving tasks and a final examination. The
nature of the assessment required students to work individually (for online quizzes and
the examination) and in groups (for the problem-solving tasks), to use diverse skills
(i.e., practice drill, synthesising multiple techniques to solve problems), and, more
importantly, to produce new knowledge by co-constructing the acquired knowledge in
a contextualised situation.
Introduction 23
1.9.2 Functional context of Unit S
Unit S (Science) was offered to first year students as an introduction to quantitative
research methods in science. It provided foundational knowledge of mathematical and
statistical methods routinely used in science, with an emphasis on developing students’
quantitative skills when analysing, simulating and modelling scientific data. As the
unit used experiential learning and teaching methods, all content was heavily
contextualised, with real-world examples used for the major part of tasks (except for
the introduction of the theoretical concepts).
A blended learning approach was used to deliver the content of the unit, using
an experiential learning and teaching methodology along with on-campus contact
hours, complemented by a rich online environment. The on-campus component used
three types of timetabled activities: two hours of weekly lectorials, one hour of
computer laboratories, and two hours of workshops. While lectorials provided
theoretical content, practiced during workshops, the computer laboratories offered the
opportunity to practise realistic data handling (analysis, modelling, and visualisation)
and application of the R statistical software. All designed tasks were based on real-
world examples, taken directly from scientific or industry contexts, but modified for
the needs of the unit and the knowledge level of the students.
For the online component, the Unit Coordinator also used the LMS site to
provide resources, details of assessment (e.g., online quizzes, assessment rubrics and
schedule), and to facilitate students’ peer learning in the online environment by
including links to the unit’s Facebook site. The Facebook site was created by the unit
coordinator but maintained by a casual academic who was responsible for moderating
content of the site from the perspective of quality of contributions, and academic
integrity. The main objective supporting the creation of the Facebook site was to
provide students with a reliable alternative to the on-campus contact and to encourage
online peer learning.
The unit’s LMS site featured adaptive release functionality, employed to: 1)
ensure that the learning flow between elements of the unit was made salient to students;
2) promote self-regulated learning by encouraging a study routine and spaced practice
(i.e., practice quizzes, distributed assessment); and to 3) encourage self-directed
learning. Each week, when accessing the site, students encountered a page containing
only the unit schedule and assessment information, with other learning resources
24 Introduction
hidden. To release the content, students were required to read the weekly revision and
preparatory documents and to take a practice test. The revision activities varied in
nature: descriptive questions, quizzes, or short Problem Solving Tasks (PSTs).
Assessment was composed of progressive practice quizzes, to be completed in
selected weeks throughout the semester, four problem solving tasks spread throughout
the semester, and a collaborative scientific article to be submitted at the end of
semester. Practice quizzes were to be completed individually and provided immediate
feedback to students. The PSTs were also individual. For the collaborative scientific
article students were required to work in groups of three to six to collect, analyse, and
interpret appropriate scientific data, and write an answer to a scientific question in the
form of a scientific article.
1.10 RESEARCHER REFLEXIVITY
There is a growing literature on the problem of potential researcher bias caused by a
researcher’s personal position in relation to their research (Nightingale & Cromby,
1999; Malterud, 2001; Palaganas et al., 2017). Although the problem concerns all
disciplines, all researchers, and all types of research (i.e., qualitative, quantitative,
mixed), it appears that for the social sciences, with their common use of qualitative or
mixed methods research, the potential for researcher bias is high. To address this
problem, researchers need to become self-aware about the impact their frame of
reference, especially their ontological and epistemological positioning and
preconceptions, have on their research topic, “the angle of investigation, the methods
judged most adequate for this purpose, the findings considered most appropriate, and
the framing and communication of conclusions" (Malterud, 2001, p. 483-484). Such
self-awareness, complemented by a systematic analysis of the impact of researchers’
positioning on their research, has been called researcher reflexivity (Nightingale &
Cromby, 1999; Malterud, 2001; Dowling, 2006; Hesse-Biber, 2007; Palaganas et al.,
2017). Palaganas et al. (2017) observe that researcher reflexivity is both a concept and
a process. As a concept, reflexivity requires a certain level of self-awareness, or a
consciousness of one’s frame of reference, and its impact on all aspects of research
(e.g., ontological and epistemological foundations, design, data collection, data
analysis and interpretation of findings). As a process, reflexivity requires from a
researcher systematic introspection, analytical self-investigation into the ways their
Introduction 25
frame of reference impacts on their research, in order to be able to apply appropriate
filters that may limit researcher subjectivity and bias.
Several techniques have been identified that enable and promote reflexivity, such
as keeping a reflexive journal, conducting research in teams, cautiously planning data
collection and sampling, carefully conducting data analysis methods using inductive
and deductive methods, using a theoretical framework for data interpretation, or
sharing research accounts in the form of publications that report on researcher’s
ontological and epistemological positioning and preconceptions (Malterud, 2001;
Mills, Durepos, Wiebe, 2010; Palaganas et al., 2017). Several of these techniques (e.g.,
cautiously planning data collection and sampling, carefully conducting data analysis
methods using inductive and deductive methods, and using a theoretical framework
for data interpretation) form part of my research methodology in this study, and are
discussed in Chapter 3. Other techniques, such as conducting the research in a team,
relate to this study’s limitations and are discussed in Chapter 6. From the perspective
of this study, the researchers’ ontological and epistemological positioning and
preconceptions appear to have played a key role, with the potential of having impacted
on many important decisions, including the topic of the study, its purpose, research
methodology, interpretation of findings, conclusions drawn, and implications. Thus, it
is important to acknowledge that my decisions were impacted by the fact that I am an
international researcher with a background in ecological linguistics, interested in
investigating how the elements of a learning environment function (or dysfunction),
and with particular interest in learning design. From a professional point of view, apart
from being a fully qualified Foreign Language Teacher, I was working in the Faculty
in focus, and in close collaboration with senior faculty and academic leaders,
academics and professional staff members, on strategies related to learning and
teaching. Importantly, I was not working directly with students, my research
participants, or with sessional academic staff, both of whom took part in the study. My
direct, professional relationship with the academics teaching the units in focus was
limited to two Unit Coordinators of the units and the Principal Supervisor of my thesis,
who initially designed one of the units. This indirect relationship with the research
participants enabled me to take conscious steps to distance myself from the research
participants, data analysis and interpretation, using appropriate techniques (see
Chapters 3 and 6 for details).
26 Introduction
In addition, during the process of conducting the research I shared research
accounts in the form of publications (see the section ‘Publication during the
candidature’ for details), as recommended in the literature (Malterud, 2001; Mills,
Durepos, Wiebe, 2010; Palaganas et al., 2017). The act of writing publications and
preparing conference/ seminar presentations worked as a filter, an opportunity to
critically review my ontological and epistemological positioning and to reflect on my
professional beliefs and positioning.
I argue that my positioning, part of my frame of reference, should be seen as a
mechanism for personal quality assurance. It strongly inspired my research choices,
but also motivated my application of recommended techniques to control/ limit
potential researcher’s bias.
1.11 THESIS OUTLINE
The next chapter explores diverse foundational theories underpinning the study. First,
it introduces the research paradigm, built on ecological perspectives about human
development. Next, the key research fields are presented, critically discussed, and their
contribution to this study explained. These include learning networks and networked
learning, connectivism, connected learning, personalised learning and Personal
Learning Environments (PLEs) and Personal Learning Networks (PLNs). Chapter 2
also discusses the Activity-Centred Analysis and Design framework (ACAD) as the
learning design framework used for development of the ecological curriculum and
learning design framework of connected epistemic domains that emerged from the
study. The chapter closes with a presentation of the key concepts related to active
learning, such as self-regulation and self-direction and an important difference
between novice and expert.
Chapter 3 discusses the research methodology applied in this study. First, it
explains the characteristics of the embedded, single-case study chosen as the most
appropriate study design. Next, the chapter discusses research methods: a sequential
mixed research (quantitative first) with qualitative instruments integrated at the point
of data collection. Then, the discussion shifts towards presentation of the two types of
data collection techniques (questionnaires and focus groups), followed by an outline
of data analysis techniques, such as descriptive statistics (quantitative) and a content
analysis (qualitative). The next part of the chapter focuses on participants’
demographics and closes with ethical considerations and limitations of the study.
Introduction 27
Chapter 4 presents findings in response to the three research questions. The
findings introduce new phenomena identified during the data analysis, such as
relationships between students’ practices and their frames of reference (belonging to
one of the ecological systems), the consequences of using a trial and error approach to
learning resulting in an illusion of competence, or interference of perceptions between
the perceptions of academic teaching staff and students.
Chapter 5 discusses the findings from the perspective of the underpinning
research paradigm and their implications for curriculum and learning design.
Chapter 6 explains how the study addressed the five research needs (literature
gaps) identified in Chapter 1, discusses the overall conclusions of the study, and
presents implications ensuing from conclusions. The chapter culminates with a
proposed ecological curriculum and learning design framework of connected
epistemic domains that resulted from this study.
The thesis closes with a reflection on the importance of investigating complex
relationships between perception and action to assist learners in the process of
perceiving, considering and wisely acting on their perceptions in the process of
becoming lifelong, epistemically fluent active learners.
28 Introduction
Literature Review 29
Literature Review
This study is an in-depth analysis of complex and multilayered connections,
relationships, and interactions occurring between learners and their formal and
informal learning networks of undergraduate students enrolled in STEM-focused
disciplines at a university. As the central concept of this study is the interaction
between the learner and the environment, with special attention paid to learner’s
agency in taking up the educational affordances embedded in the surrounding
environments, this study’s research paradigm sees learning and practice as ecological
phenomena, and thus is broadly framed within ecological perspectives on human
development. These encompass works in the field of ecological psychology (Gibson,
1977; 1979; Good, 2007; Reed, 1996), and educational/ developmental psychology,
with special attention paid to the Bioecological Model of Human Development
(Bronfenbrenner & Morris, 2006), a more complex and dynamic version of
Bronfenbrenner’s original Ecological Systems Theory (EST) (Bronfenbrenner, 1979).
First the foundational concepts of the Bioecological Model of Human
Development (section 2.1) are presented and mapped against key constructs of
theoretical frameworks forming this study’s epistemology (sections 2.2 – 2.10). Some
of the theoretical lenses are more focused on analysing formal, institutional settings
(e.g., learning networks and networked learning) (section 2.2), some are looking at
interactions between the learner and informal, private settings (e.g., personal learning
environments) (section 2.4), while some other aspire to the status of a new theory of
learning (connectivism) (section 2.6). All the above-mentioned foundational theories
attempt to describe complex phenomena occurring within learning networks of
contemporary learners. In addition, the Activity-Centred Analysis and Design
(ACAD) framework (section 2.2.1) is briefly discussed as the instrument used to
design the conceptual framework emerging from this study.
As the concept of interaction implies agency, all these theoretical lenses
assume some degree of learner agency in using the networks. For this reason, this
chapter also reviews literature on active learning (section 2.7), with special attention
paid to self-direction (section 2.7.1), self-regulation (2.7.2), and the difference between
30 Literature Review
novice and expert learners (section 2.7.3). The chapter closes with final reflections on
the literature review (section 2.8).
2.1 RESEARCH PARADIGM
The concept of paradigm is somewhat cloudy and difficult to explain. Donmoyer
(2008), defines the concept of paradigm as follows:
Paradigm is a set of assumptions and perceptual orientations shared by
members of a research community. Paradigms determine how members
of research communities view both the phenomena their particular
community studies and the research methods that should be employed
to study those phenomena. (p. 591)
This study’s research paradigm is strongly influenced by ecological views on cognition
and learning (Bronfenbrenner, 1977; 1979; 2005; Bronfenbrenner & Morris, 2006;;
Czaplinski, 2013; Fettes, 2003; Reed, 1996), concepts of learning networks and
networked learning (Carvalho & Goodyear, 2014a, 2014b; Carvalho & Goodyear,
2018; Carvalho, Goodyear & de Laat, 2017; Goodyear et al., 2015; Markauskaite &
Goodyear, 2017a), and active learning (Bjork, Dunlosky & Kornell, 2013; Koriat &
Bjork, 2005; Soderstrom, Yue & Bjork, 2016).
As the ecological perspective is the overarching and foundational theoretical
framework giving direction to the inquiry, it is discussed in more depth. Next, the
epistemological perspectives underpinning the ontological perspective and informing
the inquiry are discussed in detail, with the relationships between elements of the
paradigm explained consecutively. Finally, this section finishes with a conceptual
representation illustrating the research paradigm of this doctoral dissertation.
2.1.1 Ontology and epistemology of the study
The ecological perspectives on learning and practice, which originated and was
developed within the natural sciences and psychological disciplines, see the process of
cognition as a dynamic relationship between human beings and their environment.
Considered from this viewpoint, cognition is environment-dependent, that is, it is
achieved through constant, life-long interaction with the environment (Fettes, 2003).
Bronfenbrenner (2005), researching within developmental psychology and
investigating the reciprocal relationships between one’s cognitive development and
Literature Review 31
the surrounding environment, defines the ecological perspective on human
development as:
[T]he scientific study of the progressive, mutual accommodation,
throughout the life course, between an active, growing human being
and the changing properties of the immediate settings in which the
developing person lives, as this process is affected by the relations
between these settings, and by the larger contexts in which the settings
are embedded. (p.107) [italics in original]
The primary objective of scientific inquiry, inspired by such a view, is to investigate
the processes of human development “as a joint function of the person and the
environment” [italics in original] (Bronfenbrenner, 2005, p. 107).
The process of cognition, from an ecological perspective, can be described in
terms of a dynamic, continuous, and interdependent relationship between an
individual, as an active agent, and their environment (Czaplinski, 2013). The outcome
of the relationship is an active agent who discovers and interacts with the environment.
Bronfenbrenner (2005) writes: “personal characteristics are distinguished in terms of
their potential to evoke response from, alter, or create the external environment,
thereby influencing the subsequent course of the person’s psychological growth” (p.
121). Hence, what is knowable to an active agent depends on their personal
characteristics or, using this study’s terminology, their frame of reference (see section
2.2.2 for definition), impacting on their capability to interact effectively with their
environment.
However, this interactive relationship between an individual and the
environment is mutualistic (Good, 2007), or reciprocal (Bronfenbrenner 1979, 2005;
Bronfenbrenner & Morris, 2006) in nature. That is, “the environment shapes the
learner’s knowledge as much as the learner shapes his/her environment” (Czaplinski,
2013, p. 10). Thus, the decisions taken by learners need to be analysed with an
awareness of the broader context within which learners live, study, work.
For contemporary university students operating between formal and informal
educational settings, using diverse types of learning networks and being immersed in
potentially rich learning environments filled with ubiquitous social and educational
affordances (see section 2.2.2 for definition), the act of effectively interacting with the
32 Literature Review
environment becomes crucial. The term effective interaction is understood as
productive interaction (Carvalho & Goodyear, 2014a, 2014b; Goodyear 2014), or an
interaction that results in the co-creation of knowledge. As it was demonstrated in
Chapter 1, to become knowledge workers (Markauskaite & Goodyear, 2017a),
contemporary university students need to develop epistemic fluency by crossing the
boundaries of diverse networks, interacting, and collaborating, with the aim of co-
creating knowledge (knowledges).
As the interaction between learners and their environment is one of the
cornerstone concepts of this study, it is important to discuss the concept of
environment itself; what its constituting elements are and how they impact on
interactions between the individual and their learning environment. The explanation
of the concept also contributes to better understanding the complexity of interactions
that research participants of this study are engaged with (see Chapter 5, Discussion).
To this end, the study uses Bronfenbrenner’s Bioecological Model of Human
Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006). The model
originated from Bronfenbrenner’s pioneering work on ecologies of learning and
practice that culminated in the development of Ecological Systems Theory (EST),
which is frequently referred to in the literature (O’Toole, Hayes & Halpenny, 2020).
The more recent and advanced version of EST focuses on processes within
surrounding systems (environment) and emphasises the dynamic, transformative, and
fluid nature of learning (O’Toole, Hayes & Halpenny, 2020). O’Toole, Hayes and
Halpenny (2020) observe that “beyond simply highlighting the agentic role of
individuals, Bronfenbrenner’s model emphasises the fluid, relational nature of learning
through a focus on personal characteristics across systems influencing development”
(p. 29).
Figure 2.1 represents the model with the types of learning networks forming the
ontology of this study mapped against the four subsystems. It is important to note,
however, that the mapping is a schematisation and, in reality, the relationships are
more complex and dynamic, transgressing the boundaries of the subsystems.
Literature Review 33
Figure 2.1: Bioecological Model of Human Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) mapped against key components of the ontology of
this doctoral dissertation.
By using the Bioecological Model of Human Development (Bronfenbrenner, 2005;
Bronfenbrenner & Morris, 2006), this study points towards the complexity of the
environment and the challenge of investigating interactions between the individual
and the environment.
The environment within which an individual functions is represented in the model as
a complex, and dynamic ecosystem composed of social groups, or subsystems, that are
interconnected and form social networks (O’Toole, Hayes and Halpenny (2020, p. 29).
The connectivity between the networks is founded on reciprocal influence between
four subsystems: 1) microsystem; 2) mesosystem; 3) exosystem; and 4) macrosystem.
34 Literature Review
The first subsystem is defined as follows:
1. Microsystem, the most immediate, surrounding environment, impacts on the
individual’s daily life and has direct influence on individual’s development
(O’Toole, Hayes and Halpenny, 2020). The system is composed of family,
peers, close friends, educators (e.g., teachers), or health services (e.g. family
general practitioner) (Berk, 2001; Perron 2017). The relationship between
the individual and the environment within the microsystem is of a bi-
directional nature; that is, the environment affects and influences the
individual just as the individual affects and influences the environment.
Bronfenbrenner (2005) pointed towards the important role microsystems
play in an individual’s development by hosting the interactions that, over a
prolonged period of time, become increasingly complex. Such “enduring
forms of interaction in the immediate environment” (O’Toole, Hayes &
Halpenny, 2020, p. 29) are described by Bronfenbrenner as proximal
processes, to emphasise their dynamic and relational nature.
From the perspective of this study, the concept of a microsystem, especially the idea
of a bi-directional relationship between learners and their learning networks and the
concept of proximal processes, attract attention for two reasons. First, it is important
to create an engaging educational environment (physical and virtual) that provides
learners with the opportunity to enter into productive interactions with their immediate
peers, and/or classmates (see Chapters 4 and 5 for the important difference between
the notions of a “peer” and a “classmate”). Second, while designing formal learning
networks, academic educators should consider the important role peers and classmates,
but also family members, can play in the learning process of an individual. It is
important to consider the educational affordances offered at the microsystem level, to
skilfully capitalise on their potential for learning. This, however, requires not only an
appropriate learning design, but also, most importantly, learner agency in realising and
taking up the affordances through self-direction. Hence, the analysis of formal learning
networks, used in this study, provides insight into ways of approaching the problem of
appropriate use of microsystems in learning design. As for informal, personal learning
networks, due to the difficulty of accessing them, this study has only investigated the
connections between formal and informal learning networks, at a few points where the
boundary between these two types of learning networks is blurred, for example when
Literature Review 35
enquiring about assistance with learning from members of the microsystem such as
family members.
The second subsystem in Bronfenbrenner’s model is mesosystem.
2. Mesosystem consists of “connections between microsystems that foster
development” (Berk, 2001, p. 26). They are the same constituents as those
in the microsystem, but more numerous and entering into more nuanced
interdependencies, while engaging in bi-directional relationships (Berk,
2001; O’Toole, Hayes & Halpenny, 2020; Perron 2017). These relationships
can be constructive and complementary, such as collaboration with peers
towards a shared goal; based on tension, such as dealing with conflicting
priorities between peers and classmates; or founded on conflicts between,
for instance, personal interests and academic/ professional obligations.
Furthermore, the relationships are bi-directional in a double sense; that is,
they occur between the different constituents of the environment (e.g.,
between peers, or between peers and classmates) and between the individual
and the constituents (e.g. individual vs peers, individual vs classmates). The
interactions between different microsystems within mesosystem, and
between mesosystem and other systems are crucial for enabling proximal
processes to occur. Lack of interaction, known in the literature as
disjuncture, signals lack of connections between different systems, which
may impede the learning. O’Toole, Hayes and Halpenny (2020) write:
Unfortunately, the structure of many formal educational
settings fails to support the development of linkages
across mesosytems and may erect barriers to transfer of
learning. In bioecological terms, this is known as a
‘disjuncture’ in the mesosystem […]. (p. 32)
The awareness of the mesosystem and its impact on an individual has important
consequences for learning design, especially from the perspective of the process of
transitioning from formal to informal learning networks. Not all relationships are of
the same nature; some friends, peers or classmates will be considered closer than
others, or will have different roles assigned to them (e.g., knowledgeable classmate, a
friend/confidant). When designing collaborative learning activities using learning
networks, this important difference should be considered to raise an individual’s
36 Literature Review
awareness of, for example, effective strategies of working in teams (i.e. with peers, or
classmates, as opposed to friends). The type of learning network appropriate for this
work (e.g., formally designed learning network) will differ from the personal learning
network created and managed by the individual. Learners could be made aware of this
difference, but also of the potential benefits of skilfully designing and managing their
personal learning networks to create a subtle net of connections that will provide
appropriate assistance, when needed. Otherwise, the above-defined disjuncture may
occur, and effectively impede the learning process.
The third subsystem forming the Bioecological Model of Human Development
(Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) is the exosystem.
3. Exosystem is the broad social environment that does not directly affect the
development of an individual, but still has an indirect impact on the person
(Berk, 2001; O’Toole, Hayes & Halpenny, 2020; Perron 2017). This
subsystem includes people (e.g., neighbours, other university students,
professionals), social and mass media (e.g., Facebook, Twitter, TV stations,
radio stations), and services and systems (e.g., government, healthcare,
education, social and political systems).
From the viewpoint of this study, the exosystem does not affect students directly but
indirectly, by influencing learning activities, designed and anacted following the
policies and procedures. That is, the changes implemented in the system (e.g.
university policies and procedures, government education policy) do impact on the
relationships the individual engages with, and on the quality of interactions resulting
from these relationships. For instance, the university vision and ambitions for the
future will strongly impact on curriculum and learning design. This is particularly
important when analysing potential blurring of boundaries between formal and
informal learning networks, as the modified interactions between students, the
university and the exosystem may inspire important changes to the curriculum and the
ways in which the curriculum is delivered.
Finally, the fourth and the last subsystem is the macrosystem.
Literature Review 37
4. Macrosystem is a system of values, customs, traditions, attitudes, beliefs,
or, in short, cultural principles that underpin a society (Berk, 2001; O’Toole,
Hayes & Halpenny, 2020; Perron 2017). Again, it does not directly impact
on the individual, but, indirectly it impacts on every aspect of an individual’s
life. This is an overarching concept, and covers the way a given society
works. Despite the unequal relationship between the individual and the
macrosystem, (i.e. between the dominant culture and the individual), an
individual can still influence the relationships. For instance, Perron (2017)
observes that in the context of higher education, “college students
conversely have influenced this system in a bidirectional manner” (p. 201),
by participating in organised actions, petitioning or advocating for change.
The current global discussion on the nature and shape of higher education, or the
parallel discussion about the importance of STEM education for the future of economic
growth and development, are examples of macrosystems indirectly impacting on
individuals’ academic life (section 1.5). This can take the shape of new, innovative
curricula being designed and implemented, but also raises the importance of personal
learning networks and learning network environments (sections 2.4 and 2.5), and,
individual’s agency in connecting, interacting, dynamically transgressing the systems,
acquiring new knowledges and learning about diverse types of knowledges.
In his later work Bronfenbrenner introduced two additional systems to the
model: the chronosystem and genetics. The chronosystem recognises human
development over time and acknowledges consecutive changes in individuals’ frames
of reference. Conversely, the genetics system looks at the biological, genetic
inheritance influencing an individual’s development. As this study is analysing the
environment at a given moment in time, in a particular context (STEM undergraduate
context), and within a particular situation (particular units of study), the two additional
systems are beyond the scope of this study and for this reason are not included in the
overarching research paradigm.
Although Bronfenbrenner focused on child development, the Bioecological
Model of Human Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris,
2006) recognises that learning occurs through time and across spaces, and provides
“a template to understand human behaviour throughout the developmental lifespan”
(Perron, 2017, p. 199). Thus, it is argued that the model can be extended towards
38 Literature Review
application within contemporary university learning environments. However, this
poses particular challenges, especially with regards to applications of
Bronfenbrenner’s theory in the context of HE and in particular when related to STEM-
focused disciplines. While there are numerous empirical studies in the field of early
childhood education (Hayes, O'Toole & Halpenny, 2017), special needs/inclusive
education (Brown & Ward, 2018), social sustainability (Davis & Elliot, 2019), or
indigenous studies (Manning, 2017), among others, the empirical investigations of
Bronfenbrenner’s model in the context of STEM-focused disciplines offered at
university level appear to be scarce. A search of the scientific databases (e.g., QUT
database, ERIC plus Education) using key search words such as STEM, higher
education, Bronfenbrenner, Ecological Systems Theory, Bioecological Model of
Human Development, yielded only a few results. Most of the identified studies had a
socio-cultural or socio-psychological focus, such as a study that investigated
participation of women in STEM disciplines at university (Christie, O’Neill, Rutter,
Young, & Medland, 2017). No empirical studies investigating the application of
Bronfenbrenner’s model in the context of learning networks and networked learning
at university level were found.
Furthermore, the choice of the Bioecological Model of Human Development
(Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006), as the ontological paradigm
has also impacted on the epistemological perspectives adopted in this study. The broad
epistemological pillars discussed below support the research and guide the study at all
its stages, from literature review to discussion. The following four theoretical
perspectives inform the study:
1. Learning networks and networked learning theories provide a background
for research into the concept of learning networks, their architecture and
student respondents’ networked learning practices occurring within them.
2. Connectivism and connected learning refer to the networks from the
perspective of theories of learning and cognition. That is, connectivism and
connected learign focuse attention on the importance of learners’ agency
when creating their own Personal Learning Environments (PLEs).
Literature Review 39
3. Active learning is discussed predominantly from the perspective of theories
of self-regulated and self-directed learning. This section makes a distinction
between these two similar overlapping, yet distinct, concepts.
Figure 2.2 illustrates the research paradigm of this doctoral dissertation.
Figure 2.2: Research paradigm of this doctoral dissertation.
The model illustrates how the above-mentioned concepts build on each other and are,
to some extent, complementary. They expand on each other and they enhance each
other’s theoretical underpinnings. For instance, while learning networks theory looks
at the structure and composition of the networks, networked learning practices assist
with investigating the learning process within learning networks. Connected learning
allows for a conceptual understanding of the idea of networked learning, especially the
concept of distributed, actionable knowledge. As for active learning theory, it assists
with analysing the uptake of opportunities for learning, provided by a learning network
and enacted through networked learning practices, from the theoretical perspective of
self-regulated and self-directed learning.
Together, the epistemological underpinnings are embedded in ontological perspectives
on the ecology of human development. All theoretical underpinnings combined
applied to investigate ways of assisting STEM undergraduate students in becoming
active, epistemologically fluent, lifelong learners.
The next section draws on the literature in relation to learning networks.
Research on learning networks and networked learning practices is interdisciplinary
40 Literature Review
by definition, uniting researchers from diverse fields, such as education, science of
learning, media studies and/or design. Investigations are seen as “boundary-crossings
in and across” places, spaces, cultures, pedagogies, and practices (Kumpulainen &
Sefton-Green, 2014, p. 8). In short, research on learning networks and networked
learning practices is anchored in diverse epistemological frameworks and the most
relevant ones to this study are discussed below.
2.2 LEARNING NETWORKS
This section begins with a short review of the literature reporting on empirical studies,
and next focuses in more detail on literature reflecting theoretical frameworks and
concepts applied within learning networks and networked learning practices.
The literature reporting on empirical studies within learning networks and
networked learning is abundant; to cite only a couple of high-impact publications: The
design, experience and practice of networked learning, edited by Hodgson et al.
(2014), The architecture of productive learning networks, edited by Carvalho and
Goodyear (2014a), Research, boundaries, and policy in networked learning, edited by
Ryberg et al. (2016), Place-based spaces for networked learning, edited by Carvalho,
Goodyear and de Laat (2017), Networked learning: Reflections and challenges, edited
by Dohn et al. (2018), or Networked professional learning: Emerging and equitable
discourses for professional development, edited by Littlejohn et al. (2019).
Hodgson and her colleagues (Hodgson et al., 2014) identified three overarching
research themes emerging from empirical studies: 1) the design and creation of
networked learning spaces (Dohn, 2014; Gleerup et al., 2014; Hannon, 2014); 2) the
implementation of the design in practice for learning (Perriton & Reynolds, 2014;
Rienties et al., 2014; Walker, 2014); and 3) the practice of informal (private and/or
professional) learning spaces (Holmes & Sime, 2014; Schreurs, 2014; Üulüsoy, de
Haan & Leander, 2014). The analysis demonstrated a broad spectrum of research foci,
interests and ideas, all exploring how the modern society uses the environment (i.e.,
places, spaces, tools, other social agents) to network and collaboratively learn. As the
environment is the cornerstone of the concept of learning network and networked
learning and is constantly changing, thus the research focus needs to adjust to reflect
the changes in the environment. Ryberg and Sinclair (2016) observed that new themes
making appearance within the empirical studies are shifting towards questions around
Literature Review 41
policy in networked learning, e-learning and Technology-Enhanced Learning (TEL)
(Hayes, 2016), with a focus on, for example, the policy surrounding the use of
databases for learning and teaching, or the application of algorithms to “govern, shape
and sculpt learners’ future lives and their lifelong learning trajectories” (Williamson,
2016, p. 41). The discussion, still held from the perspective of the policy (and politics),
also included questions related to the role of the Learning Designer in the process of
2016).The nature of the relationship between academic teachers and learning designers
remains in the centre of interest of research within networked learning, and is also
discussed in this study (section 5.3).
The literature also indicates that, with increasing interest in exploring the
potential of informal learning networks on one hand, and the raising importance of
developing learners’ epistemic fluency on the other hand, some authors focused on
exploring learning networks and networked learning in the context of online education
offered by HE institutions, especially from the perspective of enabling professional
learning/ experiences (Alexander & Fink, 2018; Czerniewicz, 2018; Lee, 2018;
Nicolajsen, 2014). Within the latter research direction, the investigation of personal
learning networks (PLN) is an interesting example of researching the ways of bridging
the gap between formal and informal learning networks.
A PLN can be described as a “network of people a self-directed learner connects
with for the specific purpose of supporting their learning” (Rajagopal, Verjans, Sloep
& Costa, 2012, p. 1). Such a definition implies a high level of learners’ agency,
autonomy and effectiveness in managing their learning by formulating their learning
needs, setting learning goals, and purposefully selecting learning strategies for
achieving these goals (Milligan, Littlejohn & Margaryan, 2014). While within the
formal educational environment, self-directing skills are still being developed by
students, they become crucial for professional life where the capacity of successfully
forming and effectively using personal learning networks is of significant importance,
as the contemporary workplaces are becoming increasingly trans-, multidisciplinary.
Thus assisting graduates how to construct and use their PLNs effectively contributes
to the development of their epistemic fluency. Research (Johnsson, Boud & Solomon,
2012) indicates that, in a professional context, interaction with members of a PLN
promotes informal learning by presenting alternative solutions to discussed problems
and inspiring new ways of thinking, which results in the co-construction of knowledge.
42 Literature Review
Research also indicates that the educational affordances of personal learning networks
are still not commonly perceived and taken up; however, one’s conscious, well-
planned and reflective actions can leverage the use of the personal learning networks
to enable purposeful learning (Rajagopal, Verjans, Sloep & Costa, 2012; Sie et al.,
2013).
Still in the context of HE as the network (Nørgård, Mor & Bengtsen, 2019) some
authors turned their attention towards networked professional learning (Timmis &
Williams, 2016; Littlejohn et al., 2019), while others advocated in favour of cultural
diversity or, using the Bakhtinian term, cultural hybridity where “education at mode
3 [i.e., networked] university invokes entanglements and nested ecologies rather than
fixed knowledge or socio-economic measurability” (Nørgård, Mor & Bengtsen, 2019,
p. 76).
The ecological aspect of learning network and networked learning is also placed
in the centre of interest of this study, underpinned by ecological models of human
development (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006).
As stated in section 1.2, the purpose of this study was to inform research into
learning design, leading to ways of designing evidence-informed, context-specific,
learning experiences to assist first year Science, Technology, Engineering, and
Mathematics (STEM) undergraduate university students in becoming epistemically
fluent, lifelong active learners. The emphasis on the evidence-informed aspect of this
research makes it predominantly a conceptual study. For this reason, the next part of
the literature review provides an overview of theoretical frameworks and concepts
used in the literature that have underpinned the design of the study, influenced the
discussion and impacted on the conclusions.
The description of learning networks, as used in this doctoral dissertation, builds
on works of Peter Goodyear, Lucilla Carvalho (Carvalho & Goodyear, 2014a, 2014b),
Maarten de Laat (Carvalho, Goodyear & de Laat, 2017) and, to lesser extent,
Christopher Jones (2015).
The notion of learning networks is founded on two conceptual pillars –
learning and networks. As explained in Chapter 1 (section 1.4), this study uses
constructivist perspectives on learning that see the process of learning as a
phenomenon of constructing new knowledge, during which social agents (human
beings) co-construct knowledge through their interactions in/ with the environment
(Vygotsky, 1930, 1978; Piaget, 1970; Bruner, 1966, 1990). The process transforms
Literature Review 43
both human beings (as they have learnt new knowledge) and the environment, as the
knowledge was applied and resulted in change, either physical or intellectual. Learning
within a contemporary, technology-supported and technology-focused society is a
distributed activity across “time, space, media, organizations and people” (Carvalho,
Goodyear & de Laat, 2017, p. 16). Furthermore, such distributed learning occurs
everywhere, not only within educational, formal places and spaces, and it is
simultaneously intentional and unintentional, or incidental (Carvalho, Goodyear & de
Laat, 2017, p. 17). Thus, one way of analysing and understanding the process of
distributed learning activity is by treating learning in terms of networks of people and/
or people and objects (Carvalho, Goodyear & de Laat, 2017; Hodgson et al., 2014;
Jones, 2015).
With regards to the notion of network, Goodyear and Carvalho (2014a) have
observed that the concept of network should be seen as a metaphor referring to “both
material and human connectivity” (p. 9). Such a perspective emphasises the
conceptual, invisible nature of networks built through digital connections and human
relationships.
Carvalho, Goodyear and de Laat (2017) also note that learning and networks
are in a bi-directional relationship with each other. That is, “networks are assembled
in learning and learning is shaped by existing networks” (Carvalho, Goodyear & de
Laat, 2017, p. 17). This is an important statement, as it leads to a broad interpretation
of the concept of learning network, including formal and informal environments (e.g.,
shaped through incidental learning). Moreover, such a broad perspective attributes an
important role to the act of perceiving learning opportunities offered by the
environment. Thus, it seems that learning may depend on the individual’s capability
to perceive opportunities for learning embedded in the environment. Such a
perspective thus raises questions about an individual’s capability to perceive learning
opportunities, especially within informal environments, and their awareness of the
need to improve these capabilities (in the case of low levels of perception of learning
opportunities). These challenges are discussed further in the study, especially in
chapters 4 (Findings) and 5 (Discussion).
These questions are specifically pertinent in the context of HE needing to
respond to the double challenge of satisfying the demands of the knowledge economy
(Powell & Snellman, 2004), or knowledge society (Kirkwood & Price, 2006), which
requires knowledge workers (Markauskaite & Goodyear, 2017), and of responding to
44 Literature Review
the expectations of a learning society (Kirkwood & Price, 2006), which needs
graduates who are lifelong learners.
Numerous definitions of the concept of learning network have been developed
over time, most of which reflect advances in the application of new technologies for
knowledge acquisition and learning processes (Dlouhá et al., 2013; Goodyear &
Carvalho, 2014a). Peter Goodyear and Lucila Carvalho, in their book The Architecture
of productive learning networks (2014a), have discussed some proposed definitions of
the concept, and have emphasised the conceptual difficulties they faced in grasping
the nature of a learning network. It appears that many definitions focus on the
relationship between technology, connectivity, and the process of learning (see
especially Goodyear and Carvalho, 2014a, chapter 1). Goodyear and Carvalho (2014a)
rightly observe that many advanced definitions do not explain the nature of the
network itself (i.e. what exactly is a network), do not define the appropriate unit of
analysis (e.g. the boundaries of the enquiry) and/or do not define the nature of the
learning process they support (how does the learning occur within a network). This
reflection extends to three other definitions, not quoted by Goodyear and Carvalho
(2014a), that focus on the perspective of social interaction in learning networks. As
interaction is one of the key concepts investigated in this study, the three definitions
are first presented and then discussed.
Sloep et al., (2007) define a learning network as “an ensemble of learners,
institutions and learning resources which are mutually connected through and
supported by information and communication technologies in such a way that the
network self-organises and thus gives rise to effective [lifelong] learning” (p. 548).
Building on works of Peter Sloep and his colleagues, Berlanga et al., (2008), defined
learning network as a non-organised group of diverse learners “who really only share
an interest in a particular domain of knowledge but otherwise differ from each other
in many respects. […]”. The authors later added: “it is in these networks that learners
may perform all kinds of formal and informal learning activities, in different contexts
and at the same time” (p. 445). The concept of the learning network was further
clarified by Sloep and Berlanga (2011) as “an online learning environment that helps
participants to develop their competencies by sharing information and collaborating”
(p. 56). The authors clarify that the design of learning networks aims to enhance the
learning experience of participants in “non-formal educational contexts (professional
education)” (p. 56), and can be adapted to “the contexts of formal education” (p. 56).
Literature Review 45
Interestingly, in this definition the notion of self-organisation of the learning networks
is no longer present. Sloep and Berlanga (2011) have also clarified that the learning
networks are formed by their participants, who use resources (imported or created) to
complete collaborative learning activities.
The three above-presented definitions of learning networks emphasise
important characteristics of the concept, such as co-creation of knowledge (e.g.,
collaborative activities), organic nature (e.g., self-organisation), or fostering of self-
direction (e.g., lifelong learning). However, they still fail to define the nature of the
network itself, for example what the meaning of the term environment is, or whether
a group is synonymous with a community, a team, or a circle of people. Moreover,
these definitions raise questions about the constituting elements of a learning network
(i.e., who and what the network is built from), and it appears that the artifacts (e.g.,
resources) are not considered as a part of the learning network. Participants in a
learning network use resources that are supported by the network, but do not constitute
the network. Furthermore, questions also need to be asked about the ways in which
participants of learning networks connect (i.e., what motivates them to interact). Sloep
and Berlanga (2011) have clarified that the “resources might help the participants to
do what they deem necessary in order to develop their competences” (p. 56); however,
the authors do not explain why, in the first instance, participants would use the
resources and why they would be interested in engaging in collaborative learning,
especially in non-formal contexts. Furthermore, all three above-mentioned definitions
seem to emphasise informal learning networks without explaining the learning process
they support. Although Sloep and Berlanga (2011) did mention formal and informal
learning environments and the relevance of learning networks for both contexts, the
difference between formal and informal learning networks is neither explained nor
explored. In addition, some concepts such as effective and lifelong learning also need
further explanation and exploration. Finally, these three definitions seem to only
briefly discuss the concept of learning process. How does the learning actually occur
within the learning network? How do we analyse a learning network to reveal the ways
in which the network enables the learning process?
Thus, it becomes evident that the definition of the concept of a learning
network requires clarity in terms of the nature of the network, its constituents and the
role it plays in enabling learning. This study therefore applies a definition of learning
network coined by Goodyear and Carvalho (2014a) that describes learning network as
46 Literature Review
“an assemblage(s) of tools, artifacts, people, ideas and practices” (p. 14). Put simply,
but not simplistically, a learning network constitutes a network of “people and their
relations and also (digital) tools and artefacts of various kinds” (Goodyear et al., 2015,
p.16).
The authors argue that, to inform future designs, a learning network should be
analysed in its entirety, as one unit (of analysis), with multiple constituents and
attributes contributing to its architecture. Thus, a learning network is a unit of analysis
enabling investigation of complex, intrinsic relationships between its constitutive
elements, irrespective of their nature. That is, the constituents can be digital and non-
digital entities, social structures, organisational systems, divisions of work, and/or task
distributions. A learning network allows for an analysis of learners’ emerging activities
resulting from interactions between the above-mentioned diverse constituents.
Every learning network is complex, emerging, structured, and unstructured at
the same time, some of the constituents of learning network can be designed, and some
emerge organically. Goodyear and Carvalho (2016) use the metaphor of a city to
explain the amalgamated nature of a learning network. It is like a city, partly planned,
and partly organically developed through individual actions of its inhabitants.
Learning networks have a multi-layered and multi-dimensional structure built from
multiple, diverse components. This structure impacts on the ways learning activities
are completed by supporting the process of taking action.
Goodyear and Carvalho (2014a) also add that some learning networks are
productive in nature. The attribute of productivity relates to the characteristics of
selected learning networks that enable/ promote collaborative co-creation of
knowledge. Goodyear and Carvalho (2014a) write: “we choose to use this word
because it foregrounds acts of creation rather than consumption and because of the
connotations of self-realisation and/or identity formation associated with productive
activity – with ‘work’ in its general sense” (p. 15). The authors also clarify that a
learning network can be described as productive, once it enables shared learning
resulting from a collaborative, coordinated, and purposeful activity of co-creating
knowledge (Goodyear & Carvalho 2014a; Goodyear 2014).
However, a learning network, including a productive one, guarantees neither
completion of the process nor achievement of the intended result(s), because of it being
emergent, which is one of most salient properties of complex systems (Érdi, 2008, p.
7). That is, the network only facilitates the process, which is emergent in nature, and
Literature Review 47
as such, cannot be designed. On the other hand, learning tasks, or “suggestions of good
things to do” (Goodyear & Carvalho, 2014b, p. 60) can be, and are, designed and acted
upon by learners, while completing a learning activity.
In their writings, Goodyear and Carvalho (2014a, 2016) use the term task to
highlight the fact that a task is composed of steps/ instructions/ suggestions, for a
learner to complete. The term learning activity implies a state of being active, taking
an action to complete the steps/ instructions/ suggestions of a learning task, resulting
in completion of the task, and, most importantly, learning. However, learning activity
is complex and it is a developmental process that requires de-, re-, and co-construction
of knowledge, distributed within the knowledge-building network. The complex and
developmental nature of learning poses challenges to educational research. Located in
between formal and informal educational settings, theories of learning are being
advanced to investigate the complex (i.e., social, distributed) and developmental nature
of learning (Boud & Hager, 2012). For instance, the science of learning (Sommerhoff
et al., 2018), building on research in psychology, education and neuroscience, helps to
understand how humans acquire knowledge, develop capabilities and learn skills,
which then allow learners (humans) to re-, de- and co-construct knowledge (Della
Salla & Anderson, 2012; Karpicke, 2012). Connectivism (Downes, 2008, 2010a, 2012;
Siemens, 2004; 2005a; 2005b; 2006; 2019; n.d.) emphasises the distributed nature of
learning within connected environments, and describe learning as a gradual
development of one’s abilities, facilitating the processes of adopting an identity (e.g.,
a professional identity) and of becoming (e.g., a professional) through the use of
networks. Therefore, the above-mentioned concept of learning activity can be
described as a part of the process, a state of being active. It is emergent in nature and
results in the co-creation of knowledge.
The emergent nature of learning activity has important consequences for
learning design theory. Researched from the perspective of the cognitive sciences, the
phenomenon of emergence can be described as a set of properties “that are not found
in any component of a system but are still features of the system as a whole”
(McClelland, 2010, p. 752). In other words, a complex system is constructed of parts
that do not have any particular property; however, taken together, they acquire a new,
overarching property. This phenomenon is observed in many disciplines: for example
in the natural sciences it is a transition between solid and liquid states, in biology it
can be seen in ant colonies, or, in neurological sciences it is called swarm intelligence.
48 Literature Review
All these phenomena are emergent in nature, which means the new property created
by the constituting properties is greater than the sum of all individual properties5. In
neuroscience, the power of the brain is the sum of the power of individual neurons
combined together. Reflecting on consequences of emergence for human intelligence,
McClelland (2010) explains:
But, in fact, these simple regularities [i.e., emergent consequences of
very complex neural system of human brain, my addition], are not
the essence of intelligence or the supreme achievements of nature.
When it comes to intelligence, the real stuff consists of human
success in everyday acts of perception, comprehension, inductive
inference, and real-time behavior […] as well as the brilliant creative
intellectual products of scientists and artists. (p. 752)
Due to the emergent nature of the learning process, learning experiences cannot be
designed, as “one cannot design someone else’s experience. […] Only the person who
is learning can learn. Someone involved in the design for learning can design things
that help other people learn” (Goodyear & Dimitriadis, 2013, p. 2).
From the standpoint of research within learning design, this emergent and non-
designable, yet describable nature of the learning process provided foundations for the
concept of indirect design (Goodyear & Carvalho, 2014a; 2016; Goodyear &
Dimitiradis, 2013; Konnerup, Ryberg & Sørensen, 2018). The emergent nature of
processes can be investigated, analysed and described in the form of governing
patterns (Jones, 2015), or design patterns (Goodyear, 2015), supported by learning
networks (Goodyear & Carvalho, 2014a; 2016).
Goodyear and Carvalho (2014a, 2016) point out that learning networks that
facilitate the process can be designed; however, they can only be designed for someone
(a learner), something (tasks, tools, artifacts, learning environment), or to facilitate
something (e.g. completion of a task) (Goodyear & Carvalho, 2014a, p. 11; Jones,
2015, p. 12). That is, while the learning process is non-designable, learning tasks, the
physical and social environment, and division of labour and social organisation
5 For more research on the phenomena of emergence and swarm intelligence see research related to the Gestalt Psychology.
Literature Review 49
(Goodyear, 2015; Konnerup, Ryberg & Sørensen, 2018) can be designed. Indirect in
nature, learning activity is thus a mediator between designed tasks and the outcome of
the activity of completing the task (Goodyear & Carvalho, 2016). This is the principle
of indirect design. In other words, the indirect nature of learning design means that
educators (i.e., designers of tasks) do have some influence over certain aspects of the
learning network (e.g., pedagogical approaches, teaching methods, tools used);
however, the activity of completing a task is itself emergent, hence dependent on
properties such as learners’ frame of reference (e.g., one’s identity as a learner), the
affordances (i.e. opportunities for action) offered by the learning network (i.e., an
assemblage), and the functional context (e.g., task), which triggers the completion of
the task. Jones (2015) suggests that “learning networks need to be a focus for
networked learning research because of the idea of indirect design, a key theoretical
contribution of networked learning theory” (p. 12). This study, focusing on an in-depth
analysis of the constituting elements of specific learning networks, is an example of
an application of this suggestion in empirical research.
Jones (2015) also points to the dynamic relation between the learning network
– an assemblage, human agency, and affordances. The concept of assemblage should
not be seen as a human-material physical entity, but rather as a structure composed of
humans and digital systems that interact with each other (Jones, 2015). As for the
learners’ agency, it is a dynamic relationship between their frames of reference, the
affordances offered by environments, and (some) constraints imposed by an
environment (Czerniewicz, Williams & Brown, 2009). Jones (2015) argues in favour
of considering agency as a property of a (personalised) collective being, because
within formal learning environments learners may be enacting the roles that they have
been assigned to:
I argue for an expansion of the notion of agency to include persons
acting not on their own behalf, but enacting roles in collective bodies
such as courses, departments, schools and universities. Academic
work undertaken by students takes place using available
technologies and the availability of these technologies and
infrastructures is an outcome of decisions and actions taken
elsewhere, either in the wider world or in the university. (p. 210)
50 Literature Review
This point of view aligns with the afore-mentioned ecological perspectives on human
agency. Learners’ agency does not depend on an individual’s properties only
(willingness, need, motivation to take action), but is also partially pre-designed by
others (especially within formal learning networks), partially dependent on an
individual’s properties (frames of reference), and partially emerging from the
circumstances. That is, a learner’s agency emerges from interaction between the
learner and the environment. A question, therefore, needs to be asked about the process
of perceiving emergent opportunities, taking them up (or not), and using them for
learning purposes. The “connecting idea of affordance” (Carvalho & Goodyear,
2014b, p. 260) offers the potential to explain the above-mentioned process.
Carvalho and Goodyear’s (2014b) description of the mutual influence between
the environment and the process of learning is anchored in their analytical framework,
namely the Activity-Centred Analysis and Design framework (ACAD), which they
developed to analyse the relationship between the environment and human activity,
which “is the key when one wants to understand learning” (p. 18). Therefore, prior to
discussing the concept of affordance, the next section briefly presents the ACAD
framework.
2.2.1 Activity-Centred Analysis and Design framework
Goodyear and Carvalho (2014a; 2016) argue that an appropriate analytical framework
needs to be developed that provides tools to capture the emergent nature of human
activity by looking at the environment within which the activity takes place, its
supporting structure, associated practices and their results (i.e. learning networks and
networked learning). This indirect, mediatory nature of the environment is explored
by the research on learning design (Beetham & Sharpe 2013; Carvalho & Goodyear
2014a; 2014b; 2014c; 2018; Carvalho & Saunders, 2018; Goodyear & Dimitriadis
2013; Goodyear & Carvalho, 2014a; 2014b; 2016; Goodyear & Retalis 2010; Jones,
2015; Laurillard 2012), resulting in the concept of indirect learning design (Goodyear
& Carvalho, 2014a; Jones, 2015), as discussed in section 2.2 (Learning networks).
Inspired by concepts from architecture and informed by ideas from design thinking,
Goodyear and Carvalho (2014a; 2014b; 2016) developed the ACAD framework to
investigate learning networks and networked learning practices with the aim of
identifying “reusable” learning design principles “to inform the design practice”
Literature Review 51
(Goodyear, Carvalho & Dohn, 2016, p. 94). Figure 2.3 illustrates the framework as
originally developed by Goodyear and Carvalho (2014b, p. 59).
Figure 2.3: Activity-Centred Analysis and Design (ACAD) analytical framework, adapted from Goodyear and Carvalho, (2014b, p.59).
Goodyear and Carvalho’s (2014a) starting point is the human activity that
triggers learning. The authors observe that “there is no experience without activity, nor
any learning” (p. 18). The authors further argue that in the context of institutionalised
education, activity (emergent thus non-designable) mediates a task (designable) into
an outcome (expected). The activity is situated physically and socially between set and
social designs. The former encompasses spaces, places, and artefacts, while the latter
encapsulates people and the social roles they fulfil. Goodyear and Carvalho (2014a)
also clarify that both types of settings are partially designed and partially organically
grown, or “partly co-configured in use” (p. 19).
This initial design was further fine-tuned by Goodyear, Carvalho and Dohn
(2016) and by Carvalho & Saunders (2018). The fine-tuned version of the framework
considers the learning activity as situated within three designable components:
physical, social and epistemic (Carvalho & Saunders, 2018). The physical design (set
design) encompasses considerations about structures of networks: places, spaces tools
and artefacts (material and digital) made available to, or self-organised by, learners
and the ways they enable the designed tasks to be enacted by learners. The social/
organisational design includes considerations about social agents involved in the
learning network and the ways they will enact the designed learning tasks. These are
52 Literature Review
predominantly students, but also other social agents such as peers, academics, or other
experts, not always perceived/ considered by the teacher-designer. The design of
learning tasks, or the epistemic design, involves considerations about the discipline
knowledge content, relevant and effective pedagogies, and ways of conveying the
information about the task to learners. Finally, Carvalho and Saunders (2018)
identified a fourth element of the framework, co-creation and co-configuration
activities (Carvalho & Saunders, 2018, para 4), or the designed tasks and the emerging
activities that enact the tasks.
The intended outcome of the activity is learning. Goodyear, Carvalho and Dohn
(2016) note that the understanding of the outcome within the ACAD framework
depends on one’s understanding of the learning process. This can be understood as
“acquisition, participation, knowledge creation or creating epistemic
tools/environments” (p. 103). For instance, looking at learning outcomes as knowledge
creation, Carvalho and Saunders (2018), in their application of the ACAD framework
to analyse an undergraduate unit of study, talked about “co-creation and co-
configuration activities” (p. 3). Markauskaite’s (2018b) work goes in the same
direction and argues that teaching should be seen as enabling co-configuration of
knowledges, learning should be seen as co-construction and “conscientious
inhabiting” of knowledges, and learning design should be seen as relational design,
that is, co-designing for knowing. Markauskaite (2018b) observes that, to promote
epistemic fluency, modern education should “focus on learning that enables students
to re-imagine their future, co-assemble their own environments, and co-create
actionable knowledge that runs away outside the educational institutions” (para 2).
Figure 2.4 illustrates the re-interpreted model that inspired the ways in which this study
understands the concept of a learning network.
Literature Review 53
Figure 2.4: Re-interpreted Activity-Centred Analysis and Design framework,adapted from Goodyear and Carvalho (2014a).
Educators (teachers) design specific learning networks that is assemblages of
places, tools, tasks and social structures, to be used by learners while completing
(designed) learning tasks. These learning networks are created by three sets of designs:
epistemic, set, and social. Epistemic design refers to the choices educators make when
designing educational tasks. These choices (i.e. epistemic design) are influenced by
educators’ ontological stands (philosophy of learning and teaching), and their views
on the roles each component of the assemblage should fulfil. Educators’ frames of
reference strongly impact on the ways they design functional contexts, that is, learning
experiences composed of individual learning tasks. Thus, the foundations of choices
are educators’ ideas they hold about education which they implement through choices
they make, for instance by selecting specific approaches to learning and teaching
(section 4.1.2). Here, the term ideas is used a concept encompassing a range of
educators’ perspectives, convictions, opinions, beliefs, etc. Social and set designs are
only partially designed (controlled) by educators. An important part of these designs
is beyond the reach or the direct control (and influence) of the educators. Finally,
learning is a result of de-, and re-construction of knowledge and its co-configuration,
resulting in co-creation of new knowledge. The designed task is taken by a learner who
re-interprets the task according to the learners’ frame of reference and functional
context through the process of emergent activity. That is, the learner, the agent of
54 Literature Review
change, perceives an opportunity for learning offered by a knowledge distributed in a
learning network, acts upon it, that is, takes from and contributes back to the learning
network, and in this way makes the changes needed for co-configuration and co-
creation of distributed knowledge to occur. As a result, an outcome, a co-configured
new knowledge is produced. Only when the new knowledge is fed back to the learning
network, that is, when learner produces new knowledge, a learning network can be
considered as a knowledge-building, productive learning network (Goodyear, 2014;
Goodyear & Carvalho, 2014a, 2014b).
The ACAD framework enables analysis of learning activities within complex
learning environments, as well as the relationships and inter-dependencies between all
constituents of the learning situation. The framework influenced the ways the learning
environment of the two STEM-focused units under investigation was analysed. For
instance, the framework influenced the detailed description of the constituting
elements of learning networks and the identification of the learning affordances they
offered (see Chapter 4 for details). The framework also inspired (and was used) to
develop an ecological curriculum and learning design framework of connected
epistemic domains, an emerging outcome of this study, as discussed in Chapter 6.
Goodyear and Carvalho’s interest focused on teaching as design, the kind of
educational work that sets things in place prior to a learning activity (Goodyear,
Carvalho & Dohn, 2016, p. 95), and on the role of teachers as designers. The ACAD
framework certainly provides a powerful analytical instrument to investigate such
questions as: “What is the composition of learning networks?” or “How does a
particular learning network support the completion of a learning task and the
achievement of a learning outcome?” However, from the perspective of this study, the
framework does not provide an answer to the question “How does the environment
affect the completion of learning tasks and enactment of learning activities?” More
precisely, it appears that the following questions need further investigation: “How are
the learning opportunities embedded within the designed task perceived by learners?”,
“Which opportunities are taken up, and why?” Answers to these questions are
important as they may contribute to the development of a body of knowledge around
ways to promote active learning, (i.e., self-regulated and self-directed learning), as
they focus on learners’ agency.
It is important to note that Carvalho and Goodyear (2014a) did address, to some
extent, the above-mentioned problem and proposed the application of “connecting
Literature Review 55
ideas” (p. 260), in the form of affordance, legibility and interpretation, to analyse the
“How” questions. A brief critical discussion of these concepts is presented in the next
section, with an emphasis on the concept of affordance.
2.2.2 The concept of (learning) affordance
Affordance can be seen as a bridging concept between a learning network and
networked learning practices. Goodyear and Carvalho (2014a) use the concept of
affordance to explain the interaction between the learner and the environment
(Goodyear & Carvalho, 2016): “this is a more complex notion of affordance – and we
want to argue that this more elaborate notion is part of what allows skilled practitioners
to configure their working environment efficiently and effectively (p. 223)”. Goodyear
and Carvalho (2014b), following the work of Norman (1988, 1999), define affordance
as perceived and actual properties of an object (p. 52). In explaining the concept of
connecting ideas (Carvalho & Goodyear, 2014a, p.260), the authors write:
The affordances of an object suggest to the perceiver what use it
might be to them and how it might be used. Legibility is a quality of
a place that allows people to ‘read’ and ‘orientate’ themselves in it.
When someone ‘reads’ a place, this may include (quickly)
perceiving some of its affordances, as well as more slowly
interpreting its other qualities. [my emphasis] (p. 260)
The above-presented definition of the three connecting ideas applies to epistemic and
set designs. The concept of affordance entails important implications for learning
design, as it illustrates the concept of indirect design of learning tasks. The emergence
of activity is due to the fact that the learner first perceives the affordances and then
decides whether to take them up or not. The ways educators understand the concept of
affordance and its role in enabling/supporting human interaction within the
surrounding environment impacts on the design of learning tasks, and on the type and
amount of scaffolding embedded in the designed task. Goodyear and Carvalho (2016)
describe the concept of affordance in the following way: “affordances are best
understood as relational – they are what the tool offers a particular user, or class of
users” (p. 222).
56 Literature Review
Jones (2015) presents a similar viewpoint when he observes that the concept
of affordance, due to its focus on a relationship between people and the technological
environment, “is inextricably linked to the scale and complexity of contemporary
digital networks” (p. 225).
When reflecting on the architecture of learning networks, their connections and
relational nature, Goodyear, Carvalho and Dohn (2016) note that, to design learning
experiences, one needs to understand three types of connections that occur within
learning networks: 1) between physical things and physical things (T-T); 2) human
beings and human beings (H-H); and 3) human beings and non-human physical things
(H-T) (p. 94). The most challenging for researchers to describe and analyse is the last
type of connection between human beings and non-human things (H-T). Goodyear,
Carvalho and Dohn (2016) write:
To understand the implications of connections between humans and
things (H-T), one needs a relational rather than a dualist ontology:
for example, an affordance of a thing for a person depends on
qualities of the thing relative to capabilities of the person (skills,
perceptual acuity, etc.). (pp. 94-95)
From the perspective of this study, it seems that the description of the relationships
that occur within networks focuses on the concept of connections/ connectivity, and
the affordances are seen as properties of the constituting elements. However, the
concept of connection/ connectivity does not provide answers to questions related to
the process of learning within a learning network (end of the section 2.2.1).
Furthermore, as explained later in the thesis (section 2.6), the concept of connection/
connectivity may be seen as a mean for enabling interaction between the constituting
elements of the network.
For this reason, and to enable the investigation of the concept of interaction, this
doctoral thesis uses the concept of affordance, as developed by Czaplinski (2013), to
analyse and describe the interactive and relational nature of connections between the
constituents of learning networks, including human beings and objects.
The model of affordance resulted from my investigation of the concept of
affordances of Information Communication Technologies (ICTs) in the context of
foreign language programs offered at university level. Anchoring the research in social
Literature Review 57
psychology, and writing from the perspective of social agents’ perceptions (or lack of
perceptions) of affordances, the research (Czaplinski, 2013; Czaplinski 2015;
Czaplinski, Mallet, Burrage & Psaltis, 2015, Czaplinski & Mallet, 2016) built on
Good’s (2007) model of affordance, highlighting the interdependent structure of the
concept. Seen from this perspective, affordance, or an opportunity for an action, is
nested within a functional context (particular environment or circumstances requiring
an action to be taken) which, in turn, is situated in the broader concept of a frame of
reference (social agent’s abilities, capabilities, skills, identity, etc.).
The frame of reference is defined as a set of features allowing for the perception
of affordances offered by the surrounding environment and influencing the decision of
taking them up or not. An individual’s frame of reference can be modified according
to the surrounding environment, (changing) circumstances, and under the influence of
new experiences. A social agent can be more or less aware of the frame of reference
and can control to some extent the variations occurring within it (e.g. choosing to draw
lessons from previous experiences). However, the frame of reference can also be
modified without a social agent having control over the process.
In this study, the frames of reference of student and academic research participants
were discussed as a part of their demographic information (section 3.2.3), their
previous professional experiences (academic staff, section 4.3.1) and their motivations
to study (student participants, section 4.1.1). Such a detailed description allowed for
the observation and analysis of the influence of frames of reference on their
perceptions and uptake of opportunities for learning.
The second element of the affordance, functional context, is that part of the
surrounding environment which includes particular circumstances and physical places,
facilities. It is a catalyst for an action, which facilitates the perception of affordances
by the social agent and triggers their uptake. The functional context can provide
examples of good practice, concrete examples of applications of already-identified
affordances and in this way can provoke the action of their uptake. The change in
functional context (e.g., inclusion of a new technological artefact or modifying a task)
influences changes in one’s frame of reference and impacts on changes in the
perception of affordances. In this study, functional context was provided by the two
science and engineering units of study. The functional context of these units was
described in detail in section 1.7.
58 Literature Review
Finally, the affordance is an occasion to satisfy one’s needs, an opportunity for
an action, which can be perceived or not, taken up or not. Its perception depends on a
social agent’s frame of reference. However, the action of taking it up is governed by,
and occurs within, the functional context. Any modifications in the frame of reference
and/or functional context influence the perception and uptake of affordances (p. 104).
Figure 2.5 represents graphically the model, as interpreted by Czaplinski (2013).
Figure 2.5: Model of affordance (Czaplinski, 2013, p. 105).
The figure attempts to capture the interaction between the three components of
the concept of affordance, and emphasises the emergent and dynamic nature of
perceiving appropriate (i.e., suitable) opportunities and taking them up (or not). It also
focuses attention on the process of selecting opportunities, as depending on two types
of perception (i.e., properties and opportunities), and the need for this to be embedded
in the environment (functional context). As Hutchby (2001) points out, affordances
may vary depending on the artefact and the environment they are in; however they are
not “freely variable” (p. 26), that is, they are somewhat limited by the context and the
environment. Finally, the graphic also sheds some light on the idea of agency (i.e.
taking action, or not). The act of taking an action (agency) depends on a social agent’s
perception, which is bi-directional: self-reflective (proprioception) and directed
towards the surrounding environment (exteroception). Taking action (or not) denotes
a reaction to perceived opportunities. However, as the perception is bi-directional, both
processes of perceiving and, in consequence, taking action (or not) are therefore
emergent in nature, and thus non-designable. For learning design, again, the
implication is the idea of indirect design and this reinforces the need for closer
Literature Review 59
investigation of the constituting elements of a learning network (and the process of
networked learning).
In summary, the literature on the architecture of learning networks indicates
that researchers should consider broader aspects of human practices when learning,
including “the role of tight, fast, perception-action loops in skilled human action;
intentions emerging from situated action and perception, and a blurring of boundaries
between mind, body and world” (Goodyear, Carvalho & Dohn, 2016, pp. 103-104).
For this reason, this study uses the aforementioned definition of the concept of
affordance and argues that such a perspective on affordance provides an appropriate
instrument to analyse the interactions between the constituting elements of learning
networks. In addition, such a perspective on affordance also reflects the ontological
underpinnings of this study, as it includes the frame of reference that can, to some
extent, be compared with the concept of ecological system (see Chapter 5 for details).
2.2.3 The expanded definition of learning network
Considering the above, the following definition of a learning network has been
developed and applied in this study: a learning network is understood as an assemblage
of five key elements: 1) tools and 2) artifacts used within the two units of study under
investigation; 3) people (social agents), as the elements of the learning networks under
investigation; 4) ideas (related to learning and teaching and expressed by research
participants); and 5) practices (enacted by the research participants). This assemblage
offers learning affordances that can be perceived or not, taken up or not. Affordance,
or an opportunity for an action, is a concept nested within the broader concept of a
functional context, which in turn is nested within the concept of a frame of reference.
While the perception of affordances depends on a social agent’s frame of reference,
the decision of taking up the affordance or not is triggered by the functional context
(the foundation of the need to complete a learning task) and is enacted by the social
agent. Learning networks are productive when they support interaction leading to co-
construction of knowledge. Co-construction of knowledge is understood as the shared,
collaborative, coordinated, and purposeful processes of taking distributed knowledge
from the network, de-/re-constructing it and co-constructing new knowledge. For
example, the co-construction of new knowledge may be evidenced in the making of a
60 Literature Review
digital or material artifact, learning from/with others, assisting other learners with
learning, socialisation with others, and self-regulation of one’s own learning.
This definition naturally leads to the process of networked learning, the
process that occurs within learning networks. The learner (i.e., social agent) takes an
action which is un-designable and, in response to the need of completing educational
tasks, acts upon it. When repeated over a certain period of time, this process becomes
the learner’s (i.e., the social agent’s) networked learning practice.
This section has discussed key concepts of research on learning networks.
From the perspective of this study, the necessity of investigating specific learning
designs, contextualised within specific disciplines and situated within specific degrees
(years of study) becomes evident. Educators, and/or learning designers, need to
carefully analyse the context within which learning activities take place, the
characteristics of the students involved, the affordances for learning offered by the
environment, and the ways learners take on opportunities for learning (or not) and act
on designed tasks. However, learning networks support the interactions within them,
and thus the analysis of interactions also requires analysis of networked learning
practices, as discussed in the next section.
2.3 PRODUCTIVE NETWORKED LEARNING AND NETWORKED LEARNING PRACTICES
Many HE institutions are shifting towards more diverse approaches to learning and
teaching, embracing online learning, with varying degrees of balance between on-
campus and online delivery (Moskal, Dziuban & Hartman, 2013). The definition of
networked learning therefore needs to consider the diverse nature of the network,
digital and physical, and has to encompass key elements of the concept: the assemblage
forming a learning network and its architecture, people (e.g., social agents, learners)
and their agency, connections (i.e., digital and non-digital) between people and other
constituting elements of learning networks (i.e., artifacts, tools), and the emergent
nature of interactions that occur within learning networks and result in networked
learning practices. Furthermore, building on the work of Goodyear and Carvalho
(2014a, 2014b, 2016) (see section 2.2 above), such networked learning practices need
to be productive, that is, support co-construction of knowledge (or knowledges) as part
of a collaborative, coordinated, and purposeful activity (Goodyear & Carvalho 2014a;
Goodyear 2014). Therefore, it needs to support diverse knowledges and different ways
Literature Review 61
of knowing, and to enable epistemic fluency (Markauskaite & Goodyear, 2017a,
2017b, 2018a, 2018b).
A frequently used definition of networked learning by Goodyear, Banks,
Hodgson, and McConnell (2004) defines networked learning as “learning in which
information and communications technology (ICT) is used to promote connections:
between one learner and other learners, between learners and tutors; between a
learning community and its learning resources (p. 1)”. De Laat and Ryberg (2018) note
that this definition emphasises the essence of networked learning as an interaction
between social agents (i.e., learners, tutors, learning community) through technology,
which provides connections and facilitates relationships, and hence enables networked
learning. The process is therefore fundamentally focused on interaction mediated by
technology where interaction becomes the condition of networked learning.
The crucial role of connections that enable relationships and foster interactions
within networked learning is highlighted by other researchers (Dohn et al., 2018;
Hodgson et al., 2014; Hodgson & McConnell, 2018; Jones, 2015) who have proposed
definitions of the networked learning process. For instance, Jones (2015) stresses the
importance of connections between social agents and their surrounding environments.
Jones (2015) sees the surrounding environment in a broader context, namely as a
global environment:
I make these points to emphasise that networked learning is about
learning in this world, a world that is an assemblage of people and
machines, of politics and economics, and large historical forces in
which education and learning only form one small but essential part.
(p. 3)
In a similar way, Dohn, Sime, Cranmer, Ryberg and de Laat (Dohn et al., 2018) define
networked learning as “the learning arising from the connections drawn between
situations and from the resituated use in new situations of knowledge, perspectives and
ways of acting from known ones” (p. 205). New situations of knowledge result from
interactions between “situations” filled with people, tools, artefacts, ideas,
perspectives, viewpoints, etc. The interactions weave connections that become a
network and enable the creation of new knowledge.
62 Literature Review
Finally, an analysis of various definitions of networked learning, completed by
Hodgson and McConnell (2018), confirms the observation above. Their investigation
was conducted amongst researchers who contribute on a regular basis to research on
networked learning and revealed a set of attributes associated with this concept:
learning community; connections; reflexivity; criticality; collaboration; and relational
dialogue (p. 455). The common theme between these attributes is high quality of
interactions leading to the development of a self-regulated, self-directed and
connected, collaborative learner, or, in short, an epistemically fluent learner.
However, the interactions need to lead to engagement with knowledge, first de-
and next re-construction of knowledge, resulting in the co-creation of knowledge, as
this is the primary objective of any learning activity. Using Goodyear and Carvalho’s
term (2014a), the interaction needs also to be productive by learning how to take
action, and by engaging with the surrounding environment which is co-configured
through a learning activity but which cannot be designed (due to its emergent nature)
and occurs during a collaborative, coordinated and purposeful process.
From the perspective of this study, networked learning is a process that occurs
within the learning network; a learner perceives (or not) the opportunities for learning,
takes the decision to take them up (or not), and acts upon the decision (or not). When
repeated over a certain period of time, this process becomes the learner’s networked
learning practice. Considering the above, this study thus proposes the following
definitions of networked learning and networked learning practices: productive
networked learning takes place inside learning networks and is understood as an
interaction within assemblages consisting of learning actions taken by a learner to
complete a learning task. During this process, the knowledge is de/- and re-constructed
through participation in learning activities, and the learning environment within which
the activity takes place is thus co-configured. As a result of this process, knowledge is
co-created.
Such definitions of networked learning and networked learning practices build
on the concept of learning networks, and focus on two components that are either
observable (interacting, completing an activity), measurable (completing an activity)
or designable (learning task). They also consider the role of the learning environment
(functional context) in the process. Finally, the definitions allow for an analysis of
learners’ interactions with and within the environment (see Chapter 4).
Literature Review 63
The key challenge in this study is to investigate networked learning practices
to understand how specific learning tasks are being enacted within the specific learning
networks under investigation. Literature indicates that a careful analysis of learning
networks and networked learning practices within specific contexts and environments
can result in generalisable findings, which may inform learning design in other settings
(Carvalho, Goodyear & de Laat, 2017). That is, the in-depth analysis of specific
examples of epistemic practice, including the environment with its constituents and
attributes, and the roles these attributes play in creating productive learning, provides
ideas that can be re-used in other settings and for other designs. In short, such an
analysis creates “actionable knowledge” (Carvalho & Goodyear, 2014a; Carvalho,
Goodyear & de Laat, 2017), “knowledge that can play a significant role in guiding
action in the world” (p. 18). Furthermore, actionable knowledge can enhance the
learning and teaching approaches to assist learners with developing different
knowledges and diverse ways of knowing, which are conditions of becoming
epistemically fluent. In summary, the findings of this study, resulting from analysis of
specific learning networks and networked learning practices, will suggest relevant and
contextualised learning strategies that offer the potential to inform learning design, to
promote networked learning practices for STEM undergraduate university students,
and to assist them in becoming active learners (see Chapter 5).
One of the biggest challenges faced by researchers in learning networks and
networked learning are the blurred boundaries between formal and informal learning
networks. Often, it is impossible for a researcher (and educator) to observe
informal/private learning networks. In addition, it appears that the difference between
formal and informal learning networks is not clearly defined. Some authors for
example mention educational and non-educational environments (see above Berlanga
et al., 2008; Sloep at al., 2007; Sloep & Berlanga, 2011; Carvalho & Goodyear, 2014).
This study, however, investigated learning networks and networked learning practices
that occur within both formal and informal settings, which posed an important
challenge in terms of the conceptualisation of informal learning networks, their
description and the related research methodology. The literature on personal learning
environments offers an interesting theoretical background for describing informal
learning networks, and, in addition, provides a link between the above-discussed
literature and the ideas of connectivism that have also influenced this study. For these
reasons, the next sections provide an outline of the concepts of personal learning
64 Literature Review
environments (PLEs) (section 2.4) connectivism (section 2.5) and connected learning
in the context of HE (section 2.6).
2.4 PERSONAL LEARNING ENVIRONMENTS
The arrival of Web 2.0 digital tools and technologies sparked the development of
PLEs. Web 2.0 is a conceptual umbrella term to describe a set of applications that
enable a shift from an authoritative, institution-controlled digital system to the open
“wisdom of crowds” (Madden & Fox, 2006 p. 2). Such a description of Web 2.0
highlights the important affordance offered by the web: co-creation of knowledge
through rich interactions made possible by a set of connections, some purposefully
designed, some organically grown. This means that knowledge co-created within the
web is of an emergent nature, hence, the overall knowledge available on the web is
bigger than the sum of individual knowledges contributing to the web. Thus, Web 2.0
can be described as an ecosystem of applications that facilitate the emergence and
support of swarm intelligence. This ecosystem provides a vast offer of Web 2.0 tools
(McLoughlin & Lee, 2010) for PLEs to be created, to “support self-organised,
informal, lifelong learning and network learning” (Chatti, Jarke, & Specht, 2010, p.
79).
The literature presents diverse definitions of PLEs, some underlying the
technological aspects of the ecosystem (Siemens, 2007), some focusing on the
application of connectivist ideas (Chatti, Jarke, & Specht, 2010; Downes, 2008, 2010a,
2012), and some seeing the ecosystem as a potential, pedagogical approach (Dabbagh
& Kitsantas, 2012) that supports learner-centered, collaborative approaches to learning
and teaching, or as a resource used by a learner as a part of a larger educational
enterprise (Casquero et al., 2016). For instance, Siemens (2007) proposes a
technology-centered description of a PLE as a collection of digital tools and
technologies, open for everyone, enabling exchange and use of information, and
controlled by the learner. This is a highly technological perspective, which does not
consider the educational/ knowledge-building affordances of the web. Exchange and
use of information are not synonyms of knowledge co-creation because they lack an
emphasis on intellectual effort made by the learner to create something new, in
interaction with the environment.
According to Dabbagh & Kitsantas (2012), a PLE is a “potentially promising
pedagogical approach” (p. 3) that brings together all types of learning, such as formal
Literature Review 65
and informal, personal, academic and professional, structured and unstructured. This
perspective, although recognising the pedagogical aspect offered by Web 2.0,
overlooks the dynamic and creative nature of the interactions occurring within
personal learning environments. Moreover, “pedagogical approach” is a broad and
somehow confusing term, implying formal, structured, institutional learning, which is
not at the heart of the concept. In addition, some educational experts might discuss the
adjective ‘pedagogical’ as being strongly associated with discipline investigating
learning and teaching of children (as opposed to adult learners), but it also widely
accepted as a term to discuss learning in teaching in higher education.
Another viewpoint on PLEs is presented by Downes (2008, 2010a, 2012) who
sees them as a vehicle supporting interaction between entities forming a network and
in this way enabling co-creation of knowledge. That is, PLEs are spaces (digital and
non-digital) composed of (predominantly) free, online learning resources, where the
learner takes control of the ways the resources are managed, and also actively engages
with the distributed environment (people, networks) while co-creating knowledge (as
opposed to only consuming knowledge). This is a dynamic, distributed process
occurring through interactions with networks of people, services, and resources. As
the networks are multi-disciplinary by nature, PLEs provide learners with the
opportunity of engaging with a broad spectrum of collaborators, peers, more advanced
peers, specialists, and experts. Learning within the PLE “becomes as social as
cognitive, as much concrete as abstract, and becomes intertwined with judgment and
exploration” (Downes, 2010a, p. 21). Learning happens through conscious
engagement with the environment, through interaction resulting with a product,
namely knowledge created and critically evaluated. Such a PLE supports the process
of co-creation, which is, and should be, reciprocal as a result of taking from (the
environment), interacting, and giving back. This is opposed to passive assimilation of
knowledge, as mentioned in previous sections. Hence, to some extent, one’s PLE is
evidence of aspects of one’s knowledge. Such a perspective on personal learning
environments somehow captures their nature and points towards the challenge
educators face when designing learning experiences that would include, to some
extent, the use of students’ PLEs. First, these environments are unknown to educators,
and second, the extent to which their potential of co-creating knowledge will be used
by students depends on students’ levels of digital literacy, their ability to understand
diverse knowledges and ways of knowing, in order to appropriately engage in
66 Literature Review
epistemic practices (actioning the knowledge), in the process of becoming
epistemically fluent. This, in turn, requires well developed self-regulation and self-
direction, two key concepts supporting the construct of an active learner, as understood
in this study.
However, to support the co-creation of knowledge, PLEs need to be productive.
There are several attributes of personal learning environments’ productivity paradigm.
Apart from personalisation of the environment, and its openness, distributed nature
and high level of interactivity, the remaining attributes are “ownership, awareness and
self-regulation” (Casquero et al., 2016, p. 115).
Literature indicates that the feeling of having ownership (Tolmie & Boyle,
2000), taking control (Jones & Issroff, 2005), and monitoring processes that occur
within the PLE (Orlikowski, 2002) significantly improve learners’ agency (Casquero
et al., 2016). Awareness of what PLEs are, what/who their constituting elements are,
and what the learning processes are that occur within these environments, positively
impacts on the quality and quantity of interactions and enhances collaboration
(Casquero et al., 2016). Finally, research also indicates that for the PLE to promote
learners’ self-regulation, the personalisation of the environment needs to be supported
by appropriate types and levels of pedagogical scaffolding. Reflecting on conditions
to promote learners’ self-regulation within PLEs, McLoughlin and Lee (2010) observe
that “scaffolding need not be teacher directed, and current social software tools can be
used in ways that address learner-centred concerns for self-managed learning and
control (for example, eportfolios)” (p.33).
The primary importance of personal learning environments rests with their
transitional nature, and their connection between formal and informal settings. Their
architecture is partially designed by educators, partially designed by learners and
partially organically grown. The key challenge is to make them productive.
In summary, it is important to note the challenges posed by the emergent nature
of the knowledge within Web 2.0, which, again, has consequences for the learners’
agency. An active, agentic learner needs to acquire an appropriate level of digital
literacy to be able to not only purposefully and consciously navigate the knowledge
(diverse knowledges) available on the web, but also to acquire it, and use it in diverse
contexts (application of actionable knowledge), and thereby contribute to the co-
creation of knowledge, which, in turn should be released back to the web. This creates
Literature Review 67
the need (and urgency) to investigate ways of assisting students in becoming self-
regulated, self-directed active learners.
Moreover, as the constituents of PLEs are in large part beyond the investigation
of educators, a question may be raised as to whether explicit instruction on the ways
of forming/organising an individual’s PLE should be explicitly taught to students.
Such explicit instruction would be part of an overall digital learning strategy, including
ways of organising and effectively using the environments for the benefit of personal
learning networks. The social affordances of PLEs provide opportunities for social
agents to form PLEs (e.g. study groups, physical/ online communities, student clubs,
interest groups, professional online communities) and thus to use the technology to
interact, and to co-create knowledge (Casquero et al., 2013). In theory, PLEs foster
connectivity and allow for rich interactions. However, “there are few empirical
evidences of this vision” (Casquero et al., 2016, p. 113), especially in the context of
formal education. In short, it appears that learners are not aware of the importance and
benefits that well-structured and appropriately used PLEs can provide, not only during
their formal education time, but also after graduation. Furthermore, PLEs, if well
structured, used and systematically leveraged according to one’s personal
development and needs, can be the key factor assisting a graduate in becoming an
epistemically fluent knowledge worker (Milligan, Littlejohn & Margaryan, 2014:
Neelen & Kirschner, 2018b).
The concept of PLEs has made salient the importance of connections, the
ability of connecting with others to acquire knowledge, co-construct it and feed it back
to the network. For this reason, the next section focuses on the concept of connectivity
itself, starting from the most theoretical perspective, connectivism (section 2.5). The
discussion next shifts to the specific context of higher education and connected
curriculum (section 2.6).
2.5 CONNECTIVISM
At the beginning of the 21st century, a new framework for describing and researching
learning within a digital world has emerged, described as a “learning theory for the
digital age” (Siemens, 2004, 2005a, 2005b; n.d.). This new theoretical perspective
points to the limitations of previous theories of learning (behaviourism, cognitivism
and constructivism), as unable to consider all aspects of the learning environment.
Advocated by Siemens (2004; 2005a; 2005b; 2006; 2007) and Downes (2008, 2010a,
68 Literature Review
2012; Siemens, 2004, 2005a, 2005b, 2006; 2019; n.d.), connectivism aspires to fill in
the aforementioned limitation and to be considered as a theory of learning that offers
a framework to explain learning within complex, interconnected and technology-rich
environments. Downes (2019), one of its co-founders, defines connectivism as “a
network theory of knowledge and learning with an emphasis on the use of digital
technology to enhance and extend interaction online” (para 3).
Laying out the epistemological foundations of connectivism, Siemens (2005a)
writes that:
Connectivism is the integration of principles explored by chaos,
network, and complexity and self-organization theories. Learning is
a process that occurs within nebulous environments of shifting core
elements – not entirely under the control of the individual. Learning
(defined as actionable knowledge) can reside outside of ourselves
(within an organization or a database), is focused on connecting
specialized information sets, and the connections that enable us to
learn more are more important than our current state of knowing. (p.
7)
The following are the eight principles of connectivism, as formulated by
Siemens (2005a):
1. Learning and knowledge rests in diversity of opinions.
2. Learning is a process of connecting specialized nodes or information
sources.
3. Learning may reside in non-human appliances.
4. Capacity to know more is more critical than what is currently known.
5. Nurturing and maintaining connections is needed to facilitate continual
learning.
6. Ability to see connections between fields, ideas, and concepts is a core skill.
7. Currency (accurate, up-to-date knowledge) is the intent of all connectivist
learning activities.
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8. Decision-making is in itself a learning process. Choosing what to learn and
the meaning of incoming information is seen through the lens of a shifting
reality. While there is a right answer now, it may be wrong tomorrow due
to alterations in the information climate affecting the decision. (p.7)
From Siemens’ perspective, connectivism promotes collaboration, and privileges the
development of an individual’s skill, capability, and ability to make connections,
becoming connected, or developing one’s connectedness.
In their further work, Downes (2008, 2010a, 2012) and Siemens (2004, 2005a,
2005b, 2006; 2019; n.d.) fine-tuned the principles of connectivism and argued that
knowledge is distributed across networks. Networks are systems composed of three
major components: firstly, the entities, that is “things that are connected that send and
receive signals”, secondly, connections, that is “the link or channel between entities
(may be physical or virtual)”, and thirdly signals, or “the message sent between
entities” Downes (2010a, pp. 7-8). Entities are always a part of a larger network,
composed of at least two entities that are connected to share resources/information.
Downes (2012), distancing himself from propositional views on knowledge, considers
knowledge as “a set of connections” (p. 9) created at different levels – neural
connections that form in humans’ brains as a result of actions, and that strengthen
through experience. Other connections occur at a social level and are formed between
humans and artefacts in a digital network, or between artefacts themselves, for
example connections between “bits in a computer” (p. 16). Knowledge is the state of
change resulting from changes made through connections between entities. Discussing
the nature of knowledge in depth, Downes (2010a) observed:
Connectivism asserts that knowledge - and therefore the learning of
knowledge - is distributive, that is, not located in any given place
(and therefore not 'transferred' or 'transacted' per se) but rather
consists of the network of connections formed from experience and
interactions with a knowing community. (p.1)
Knowledge, or a network of connections, is not transferred between entities, but
develops spontaneously. However, there are two conditions for its development: “an
input and the capability of the network to self-organise” (Downes, 2012, p. 277).
70 Literature Review
Learning is the process of making connections, resulting from input and enabled by
the capacity of the network to self-organise (through experience and interactions using
a knowing community).
Therefore, learning is the formation and removal of connections in a network
between entities, or constituting elements of the network. It is a cyclical process, one
that starts with personal knowledge being fed into the network. In return, the network
also provides information that is recognised and used to modify personal networks,
enabling new connections to be fed back to the network (Downes, 2010a, 2012;
Siemens, 2004; 2005a; 2005b; 2006; n.d.). The learning process depends on the ways
one organises one’s own connections with the network; it is a “knowledge creation”
process not only “knowledge consumption” (Kop & Hill, 2008, p.2). Downes (2010a,
2012), taking an emergentist perspective on learning, has suggested that the learning
occurs, or emerges, through pattern recognition (i.e., recognition of connections)
(Downes 2010a, 2012; Kop & Hill, 2008).
Downes (2010a, 2012) has further argued that knowledge is distributed in a
network of connections and interactions within and between the networks. Networks
are learning resources, and knowledge in networks is dynamic, tacit (non-declarative)
and constructed; to learn is to recognise, interpret and replicate emergent patterns. For
instance, to learn a discipline, is “to become like a person who already knows that fact
or practices that discipline” (Downes, 2012, p. 17). Connectivism implies that the
particular entities of the network (a knowing community) recognise the constituting
elements of a set of expertise of the other entities of the network, and they organise
them into patterns which are then interpreted through their personal lenses and
successfully replicated. Although occurring within the networked environment,
learning is personal in nature, not social. Social learning, or social knowledge
management is researching, while the (individual) process of human learning is taking
up opportunities for creating and managing personal knowledge (Downes, 2010a). The
process of human learning (recognition, interpretation, replication) therefore occurs
through perpetual development of neural connections, which provide evidence (i.e.
material evidence6) of personal knowledge management. To be a learner in a
connectivist perspective is to “be a self-managed and autonomous seeker of
6 The expression “material” evidence” – is the interpretation of the researcher and was not used by Downes in his original text.
Literature Review 71
opportunities to learn” (Downes, 2014, n. p.), i.e. to develop more neural connections
through interaction with the entities in the learning network.
A discussion in the literature (Ally, 2007; Downes, 2019; Duke, Harper &
Johnston, 2013; Kerr, 2006, 2007; Kop & Hill, 2008; Verhagen, 2006) ensued
regarding the nature of the connectivist framework proposed by Siemens (2004;
2005a; 2005b; 2006; n.d.) and Downes (2008, 2010a, 2012). Questions have been
raised about whether there is a need for a new theory of learning (implying a deficit in
previous theories), and about the nature (i.e. established or developmental theory) and
type (i.e. learning or instructional) of any such theory. Many researchers (Ally, 2007;
Kerr, 2006; Verhagen, 2006) do not see connectivism as a stand-alone, new learning
theory but rather as an extension of existing learning theories (Kerr, 2006), an
instructional guide (Ally, 2007), or a new learning and teaching approach
(McLoughlin & Lee, 2010) that takes into consideration changing, environmental
factors influencing the ways humans learn (but not the process of learning itself)
(Verhagen, 2006).
In a recent overview of the last 15 years of research on connectivism, Downes
(2019, para 16-26) has reported that over the years, connectivism has been interpreted
as a theory of knowledge (Díaz & Hernádez Frutos, 2018), as it sees knowledge as a
set of connections and learning as the act of reaching for knowledge in the external
network (Al Dahdouh, 2018; Cabrero & Román, 2018). It has also been seen as a
theory of online learning with an emphasis on networked environments (Dieterle &
Clarke, 2008), as it has been useful to describe and explain the online learning process
as it is “in some important ways fundamentally different from traditional learning”
(Downes, 2019, para 20). Downes (2019, para 16-26) has further reported that
connectivism has been seen as a pedagogy for online learning (Johansson et al., 2018;
Kultawanicha, Koraneekija & Na-Songkhlaa, 2015; Mlasi & Naidoo, 2018), or a
methodology for connected learning (Attar, 2018; Cabrero & Román, 2018; Piao &
Ma, 2018).
The supporters of connectivism as a new learning theory note that it does
attempt to provide an answer to the key question in education: “How do people learn?”
(Duke, Harper & Johnston, 2013). Within connectivism it is argued that the process of
human learning occurs within PLEs, a set of connections built by an individual,
allowing knowledge to be acquired and enabling access to the experience of others
who are members of the network within the environment. Being connected, and having
72 Literature Review
one’s own PLE, significantly augments the capacity to acquire knowledge. Duke,
Harper and Johnston (2013) observe that, due to the expansion of the Web 2.0 online
environment, previous theories of learning have limited capacities to explain
distributed learning process(es). As Duke, Harper and Johnston (2013) write:
“Connectivism is defined as actionable knowledge, where an understanding of
where to find knowledge may be more important than answering how or what that
knowledge encompasses” (p. 8). Thus, connectivism allows for a closer look at the
concept of knowledge within dynamic, distributed environments, where learners take
control of knowledge acquisition. Anderson and Dron (2011) observe that
“Connectivist models are more distinctly theories of knowledge, which makes them
hard to translate into ways to learn and harder still to translate into ways to teach” (pp.
89-90).
On the other hand, arguments against considering connectivism as a theory of
learning point to the fact that connectivism is not a new approach, but an extension of
previous approaches that focus on the role new technologies play in the process of
knowledge acquisition (Duke, Harper & Johnston, 2013; Kerr, 2006; Verhagen, 2006).
According to the opponents of considering connectivism as a theory of learning, it fails
to explain how understanding is transferred and promoted (Kerr, 2006; Verhagen,
2006), and it does not explain how to enable learners to acquire knowledge at the
instructional level.
In addition, some researchers (Kop, 2011), pointed towards student agency,
especially in terms of self-directed learning, as the aspect with which many learners
might struggle within connectivist environment that requires high levels of agency and
autonomy. Downes (2019) also reported that some researchers pointed towards the
incapability of connectivism in describing how the learners form the connections “to
the variety of resources” (para 29). Moreover, critics argue that connectivism can be
applied in certain domains of knowledge, but not all. That is, not all concepts can be
transferred through connection with other entities (humans, digital entities). For
instance, challenging counter-intuitive concepts (e.g. threshold concepts) need to be
acquired, internalised and understood by learners through a process of learning that is
supported by more experienced/advanced peers (e.g. teachers, tutors, etc.) (Duke,
Harper & Johnston, 2013).
More recently, Siemens (2019) has modified his position on the concept of
network, and has pointed towards the important difference between a network and a
Literature Review 73
system. Siemens argues that the networks always exist within a structured
environment, a system. “System is a set of entities and the rules that govern their
interactions” (Siemens, 2019, para 11). Systems form an ecology, a closed entity that
determines “what can exist and what can flourish” (Siemens, 2019, para 11), while a
network is “a byproduct of the connections that exist between entities” (Siemens, 2019,
para 11). Thus, it seems that a learning network always functions within a broader,
organised and overarching structure, a system.
Two observations need to be made. First, such a way of differentiating between
network and a system draws attention to the potential use value of Bronfenbrenner’s
Bioecological Model of Human Development (Bronfenbrenner, 2005; Bronfenbrenner
& Morris, 2006) (section 2.1) as the theoretical underpinning, which can help to
understand the above-described network-system relationship. Second, it also points
towards the distinction between formal and informal learning networks (section 2.2)
as another theoretical underpinning that allows for the analysis of networked learning
processes. Informal learning networks are much more difficult to describe as they
belong to different ecological systems (Bronfenbrenner, 1977, 1986, 1994, 2005). This
reflection is empirically demonstrated in Chapter 5, in which the findings of this study
are discussed.
Connectivism certainly sees knowledge as emerging from connections.
Following the principle of emergence, the knowledge within the network is bigger than
the sum of knowledges constituting the network. Therefore, following on
Markauskaite and Goodyear (2017a, 2017b, 2018a, 2018b), the network is filled with
different knowledges and diverse ways of knowing. An individual, constituent of the
network, needs to become fluent in learning from the network, that is, “recognise the
patterns” (Downes 2012; Kop & Hill, 2008), de-/re-construct them, co-create new
patterns (knowledge) and create new patterns in the network (i.e. feed the knowledge
back to the network). In this way, an individual becomes epistemically fluent
(Markauskaite & Goodyear, 2017a, 2017b, 2018a, 2018b). As connectedness implies
interaction, an individual who only consumes knowledge or co-creates it without
sharing it, is not really connected or epistemically fluent. Only interaction ensures/
warrants fluency. This requires not only well-developed digital literacy (i.e. skilfully
using the network), but also high levels of self-regulation and self-direction. It appears
that, at least at the current stage, contemporary education systems and pedagogical
frameworks are not yet ready to face such a challenge. However, the change has
74 Literature Review
commenced and new learning and teaching approaches, as the ones described below,
are being developed and trialled (section 2.6).
The debate over the status of connectivism as a learning theory will
certainly continue for some time. Considering the ontological and epistemological
perspectives used in this study, it is argued that connectivism should not be
considered a theory of learning. Instead, it appears that connectivism could be
considered one of the theoretical frameworks of cognition, a theory that attempts to
explain what knowledge is, what different ways of knowing and different types of
knowledges are, where they reside, and finally, how to perceive, access and acquire
them. The presence of other theoretical frameworks of knowledge and knowing
support the argument that considers connectivism a theoretical framework of
cognition. For instance, there are important similarities between Markauskaite and
Goodyear’s (2017a; 2017b; 2018a; 2018b) concepts of actionable knowledge,
different ways of knowing, and conditions of epistemic fluency on the one hand, and
the above-discussed concept of connected knowledge (Downes, 2008; 2010a; 2012;
2019) that is distributed across sets of connections within networks on the other.
Knowing is a state of change. Thus, knowing where to find knowledge, and how to
engage with distributed sources of knowledge to co-create (new) knowledge, is a form
of actionable knowledge. As a result, epistemic fluency can be achieved. However,
fluency implies change, as it is a dynamic state. Being a learner means managing one’s
own change of one’s state of knowing; however, it does not explain the process of
learning.
Connectivism assumes a high level of agency from a contemporary learner
without explaining the process of learning. That is, it assumes a learner’s motivation
to learn, metacognition (knowing one’s learning patterns), and appropriate levels of
self-regulation and self-direction to be able to strategically make choices with regards
to one’s learning pathways. It also assumes an appropriate level of resourcefulness,
especially understood as learners’ digital literacy, allowing learners to skilfully
navigate a large amount of digital technologies, either provided by educational
institutions or self-selected, and aimed at enhancing learning.
Furthermore, it appears that connectivism predominantly relates to the
informal nature of learning. Informal means providing little or no scaffolding.
“Learning in connectivist space is, paradoxically, plagued by a lack of connection”
write Anderson and Dron (2011, p. 89). They explain that, due to the informal and
Literature Review 75
emergent nature of the learning environment, learner scaffolding is absent. The
connectivist environment relies on “strong entities”, i.e. leaders, followed by “wicker
entities” (less engaged participants) who engage with the environment less regularly,
and hence not fully. However, connectivism assumes learners’ agency. Anderson and
Dron (2011) also note that when scaled down and contextualised within a more
structured, institutional environment, the connectivist approach still requires a lot of
energy from strong entities, and participants who experience a connectivist approach
still report feeling lost and confused. As Anderson and Dron (2011) explain, this is
only partially due to the necessity of dealing with a new technological environment,
“the distributed nature and inherent fuzziness of goals, beginnings, and endings
implied by a connectivist approach often fits poorly with a context in which students
are taking more formal and traditional courses” (p. 89).
However, such a set of assumptions raises important questions regarding self-
regulation and self-direction, and some of them need to be verified by empirical
studies, such as this doctoral dissertation. Educational psychology research (Dunlosky
et al., 2013; Neelen & Kirschner, 2015; Roediger & Pyc, 2012) indicates a range of
learning strategies that are powerful and effective in enhancing students’ learning and
include such practices as, for example, distributed practice, practice tests, overlapping
practice, questioning or explaining to self (Karpicke & Blunt, 2011; Neelen &
Kirschner, 2015). However, research also indicates that learners need help to regulate
these practices (Ariel & Karpicke, 2018), hence they need assistance with self-
regulation and self-direction.
Notwithstanding the final positions, it can be stated that connectivism is
an interesting “school of thought” (Duke, Harper & Johnston, 2013, p. 9), which
investigates the core philosophical questions and educational challenges posed by
the development and adoption of new technologies on an unprecedented scale.
From the perspective of this study, the idea of distributed knowledge, functioning
within a network and available to be taken up and to be de-, re-, and co-constructed
and fed back to network, seems particularly interesting. It implies learner agency
in connecting to a network, interacting with human and non-human constituting
elements of that network to co-construct knowledge and, importantly, contributing
to the network by providing new knowledge to the network. However, the
questions “How does the process occur?” and “How to assist learners in using the
network productively?”, as opposed to simply consuming knowledge, remain. The
76 Literature Review
next section briefly discusses some examples of a different approaches to the concept
of connected learnign in the context of higher education.
2.6 CONNECTED LEARNING IN HIGHER EDUCATION
Within the settings of higher education, the implementation of selected ideas of a
connected learning conceptual framework is based on carefully planned and skilfully
coordinated collaboration between the learner, educators (e.g., academics, learning
designers), learning support staff (e.g., learning advisors, mentors, coaches), and
professional staff (e. g., technicians). Some stakeholders are supported by the
institution’s information systems and databases. This approach offers learners the
potential to take control of their learning by creating personalised learning pathways
to accomplish individual learning goals and objectives, which are defined and agreed
upon in collaboration with the educational institution (ELI, 2013; Smith 2013a;
2013b).
The concept of personalised learning has impacted on the development of many
research directions, each looking at personalised learning from a specific perspective,
such as learning analytics (Siemens, 2010; Buckingham & Ferguson, 20102;
Dawson & Siemens, 2015), student success (Bennett, Kapoor, Rajinder & Maynard,
2015; Cameron, George & Henley, 2012; Nelson, Clarke & Creagh, 2014), or learning
spaces (Keppell, 2014; Keppell & Riddle, 2012). Although focused on different
components of the overarching concept of personalised learning, they all converge in
their interest in promoting students’ agency by remodelling the ways learners
(students) interact with the learning environment (physical/virtual, formal/informal,
blended, mobile, practice-based spaces (Keppell, 2014; Keppell & Riddle, 2012) to
promote targeted support to learners and to enable authentic learning. From a
personalised learning perspective, a higher education institution’s responsibility lies
with equipping learners with appropriate strategies to enable them to become active
learners (Keppell, 2014). For instance, learning networks created by the academic, and
made available through the learning management systems (LMS), might be
complemented by learner-driven networks. Learning analytics might inform, in real
time, the learner’s patterns of using the learning networks. This information, in turn,
might be used by learning support services to provide targeted support in the form of
automated (e.g., reminders) and personal interventions (e.g., face-to-face sessions,
personalised assistance). Furthermore, based on learning analytics, a customised set of
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resources might be offered to learners to provide them with personalised assistance,
either on- or off-campus, within a spectrum of ubiquitous spaces designed to promote
learning. Finally, the connected learning environment might provide the student with
opportunities to connect with industry partners, social interest groups, or local
communities and to collaborate towards solutions to authentic, real-world, local or
global challenges.
Some literature (Fung & Brent, 2017; Maggio, Saltarelli & Stranack, 2016; Stiles
& Wilcox, 2016) reports on higher education institutions’ initiatives aimed at
implementing holistic approaches to connected and personalised learning. For
instance, Stiles and Wilcox (2016) have reported on Grinnell College’s initiative to
integrate learning analytics systems with existing networks to enhance early-alert
systems and predictive models to ultimately improve student retention and success.
Maggio, Saltarelli and Stranack (2016) have shared their experience of implementing
personalised learning at scale. The authors have described their experiences of
designing, developing and using a “multi-institutional, multidisciplinary, bilingual and
openly licensed Massive Open Online Course (MOOC)” (Maggio, Saltarelli &
Stranack, 2016, para 2). The design attempted to implement connected learning
principles within an xMOOC (i.e. structured, designed environment). The authors
concluded that the key factor enabling personalised learning is communities of learners
engaged in co-creation of knowledge through a shared learning experience.
One of the more recent initiatives in introducing the ideas of connected learning
in higher education settings is the “connected curriculum”, a framework that
holistically implements connected learning at the curriculum-level for the duration of
the entire student journey. Developed and adopted by University College London
(UCL), connected curriculum is a research-based university’s educational framework
(UCL, n. d.) aimed at fostering learners “agency, connectedness, and promoting their
awareness of different knowledges and ways of knowing, through rich epistemic
practices enabled by a ‘symbiotic relationship’ ” (Fung, 2017, p. 1) between teaching,
research and industry. The framework promotes learning “as a coherent personal
narrative of enquiry” (Fung & Brent, 2017, p. 61), based on the institution’s vision of
a contemporary university as “a well-tuned learning project, working at once on the
personal, institutional and societal levels” (Fung, 2017, p. vi).
The framework, composed of five dimensions, capitalises on complex types of
connections students initiate/ make/ engage in during their degree. First, research
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activities are interwoven through the learning journey and into each program of study.
This produces a backbone for a diverse catalogue of connections offered to/ required
from students. Within the framework, learners are expected to engage actively by
making connections and building relationships with:
1. Researchers and with the institution’s research;
2. Subjects within their disciplines and other disciplines (interdisciplinary
awareness, different ways of knowing, diverse knowledges crucial for
epistemic fluency);
3. Workplace learning (application of knowledge);
4. An audience through authentic assessment (product-oriented);
5. Each other during the learning journey (collaboration) and with alumni.
The connected curriculum learning framework is a complex, multi-layered, formal
learning network, designed and managed by an institution, yet still offering
opportunities for learners to extend their connections into informal settings. The
framework provides opportunities for learners to develop their capability of working
collaboratively within interdisciplinary teams, and raises learners’ awareness of
specialised, context-dependent knowledges and different ways of knowing
(connections within subjects and disciplines). By promoting epistemic practices
(involvement with research and workplaces), the framework also provides
opportunities for actionable knowledge to be applied within realistic settings to solve
given problems. Finally, it supports collaborative learning within the institution and
outside it. In short, the framework provides learners with important opportunities to
develop their epistemic fluency on their journey towards becoming epistemically
fluent “knowledge workers” (Markauskaite & Goodyear, 2017a, p. 1).
The above-cited examples of applications of connected learning in the context
of higher education, although building on some principles of connectivism (learning
networks, interest-powered learning activities, self-directed learning), seem to
combine previous pedagogical approaches, especially constructivist (Piaget, 1970)
(production-centred, academically oriented) and socio-constructivist pedagogies
(Engeström, 1987, 2000; Vygotsky, 1978) (peer-supported, collaboration, shared
Literature Review 79
purpose). However, as for connectivism, the connected learning approach invites some
reflection on its applicability.
The first point of reflection relates to the assumed learners’ agency. The
connected learning framework perceives learning as an interest-powered activity. This
might be true in the case of some students or disciplines; however the need to maintain
motivation at a high level and thereby enabling continuous intellectual effort over an
extended period of time (the constituent elements given above) may be challenging to
satisfy. In addition, in the case of formal and complex higher education curricula,
students need to take a series of core subjects, the relevance of which is not always
immediately clear to them. This can negatively impact on their motivation to learn,
resulting in low engagement and satisfaction. What if a learner loses the motivation to
learn or does not see the reason for learning a particular subject, and hence does not
engage with a connected learning approach? Would assistance provided by an
educational institution (as seen above) be sufficient to re-motivate the learner? Would
a passive learner attitude be possible within a connected learning environment? If so,
it could suggest that connected learning did not fully succeed in providing appropriate
experiences to this learner. Within connected learning, who would have the role of a
“strong entity”, leading the learning activities? Finally, how would a learner’s
achievement of standards be measured and assessed?
Furthermore, connected learning makes an assumption that learners will
already have developed, at least to some extent, their specific skills such as
metacognition, or digital literacy, allowing them to consciously and strategically make
decisions regarding their learning. However, being conscious of one’s own learning
patterns and knowing how to learn is a complex cognitive process requiring self-
awareness to be achieved through self-reflection, which results in self-regulation and
self-direction. The development of these capabilities requires facilitation, as self-
reflection can only partially be self-developed, and its principles need to be explained,
taught and then practiced. Moreover, the expectations with regards to the level of
learners’ development of digital literacy skills would need to be clearly outlined, as
the process of becoming an active learner encompasses the process of developing
specific digital literacy skills, critical evaluation of both information sources and
quality, and the productive use of digital media to benefit one’s learning.
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Again, the overarching problem emerging from the above-presented discussion
is learners’ ability to become active learners. For this reason, the next section discusses
the concept of active learning and its constituting elements.
2.7 ACTIVE LEARNING
As outlined in Chapter 1, research has demonstrated that people “often have a faulty
mental model of how they learn and remember” (Bjork, Dunlosky & Kornell, 2013, p.
417). This leads to short-time performance (Kornell, Rhodes, Castel, & Tauber, 2011;
Soderstrom & Bjork, 2015), resulting in mis-assessment of one’s own effectiveness in
the learning process, and mismanagement of learning activities. In turn, such mis-
assessment and mismanagement give learners metacognitive illusion of learning
(Kornell, Rhodes, Castel & Tauber, 2011; Soderstrom & Bjork, 2015) and often result
in applying “trial and error” techniques that creates a false impression of effectiveness.
If applied in the longer term, without any self-reflection or appropriate adjustment, the
learners mislead themselves and may develop an inaccurate impression of
effectiveness and achievement. In the literature on educational psychology, this
phenomenon is described as “illusion of competence” (Koriat & Bjork, 2005; Kornell,
Rhodes, Castel & Tauber, 2011; Soderstrom & Bjork, 2015; Soderstrom, Yue & Bjork,
2016), which can be defined as “the conditions that give rise to an overestimation of
one’s future memory performance” (Koriat & Bjork, 2005, p. 187). This, in turn, may
prevent learners from becoming effective and sophisticated in their learning practices,
that is, from becoming active learners (Bjork, Dunlosky & Kornell, 2013).
Learning, seen from the ontological and epistemological perspectives adopted
in this study (section 2.1), is a process of becoming, and includes taking on an identity/
identities through constant interaction with the surrounding environment. For the
process of becoming to be realised, one needs to be conscious, motivated, and
adequately equipped with effective strategies to enable the process of becoming.
Therefore, as explained in the Introduction (Chapter 1), this study understands the
concept of active learning as a process of becoming an active learner, that is, a
“sophisticated and effective learner” (Bjork, Dunlosky & Kornell, 2013, p. 314) who
has metacognitive self-awareness, knows powerful learning strategies and applies
them depending on learning needs. Such an active learner is also capable of monitoring
and controlling their learning conditions. As already mentioned on several occasions,
this requires learners to develop their self-regulation and self-direction.
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The concepts of self-regulation and self-direction, although originating from
different research areas, are often discussed in conjunction, due to their
complementary nature. Research on self-direction has its roots in the research on adult
learning, specifically andragogy, professional learning, and learning in a workplace
(Brand-Gruwel et al., 2014; Neelen & Kirschner, 2017; Rana, Ardichvili & Polesello,
2016; Saks & Leijen, 2014). Self-regulation, by contrast, has roots in educational
psychology, especially as it relates to task performance within institutional, formal,
school settings (Brand-Gruwel et al., 2014; Neelen & Kirschner, 2017; Saks & Leijen,
2014). Both concepts consider an individual’s ability to learn (Brand-Gruwel et al.,
2014). The relationship between both concepts could be described in terms of the
interplay between macro- and micro-levels of the learning process. While research on
self-directed learning is interested in one’s learning pathway, and hence investigates
learning processes at the macro-level, research on self-regulation analyses the
procedures and operational details of completing tasks by learners. Thus, it
investigates learning processes at the micro-level (Brand-Gruwel et al., 2014; Neelen
& Kirschner, 2017). The next two sections describe both concepts in depth.
2.7.1 Self-direction
This study uses the definition of self-directed learning as formulated by Knowles
(1975):
[A] process in which individuals take the initiative, with or
without the help of others, in diagnosing their learning needs,
formulating their own goals, identifying human and material
resources for learning, choosing and implementing appropriate
learning strategies, and evaluating learning outcomes. (p. 18)
This way of defining the concept emphasises the agentic approach taken by
experienced learners, that is, learners who take conscious control of their learning
in terms of defining their learning needs and ways of satisfying them (Rana,
Ardichvili & Polesello, 2016).
Although research on self-directed learning originated in adult education
(Brand-Gruwel et al., 2014; Neelen & Kirschner, 2017; Rana, Ardichvili & Polesello,
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2016; Saks & Leijen, 2014), its focus shifts towards learning situations requiring
application of well-developed learning skills and supported by conscious, well-
planned and targeted learning efforts. These situations include learning in the
workplace (Saks & Leijen, 2014; Van Meeuwen et al., 2013), learning in learning
organisations (Neelen & Kirschner, 2017; Rana, Ardichvili, & Polesello, 2016) or on-
demand learning that “enables individual learners to choose their learning pathways
according to their own learning needs” (Taminiau et al., 2015, p. 405). In all these
learning situations, learners need to apply their domain-specific knowledge,
continuously upskill themselves, leverage their competencies, deepen their expertise
(Van Meeuwen et al., 2013), and strategise their learning activities to capitalise on
educational affordances offered by the workplace, thereby enhancing their
professional careers (Eva & Regehr, 2005; Van Meeuwen et al., 2013). In short,
learners need to able to autonomously identify different knowledges and diverse ways
of knowing within their workplace, and take advantage of this information to improve
their knowledge, skills and competencies through self-directed learning. Self-directed
learning is therefore a combination of external (environmental, workplace) conditions
(sometimes requirements) and internal, personal characteristics.
Over the years, a discussion around the concept of self-directed learning has
led to a distinction being drawn between self-directed learning and self-direction “as a
personality trait” (Saks & Leijen, 2014, p. 191). Regardless of the subtleties of the
concept’s constituents, the contemporary model of self-directed learning was
developed by Brockett and Hiemstra (1991), and is known as the “Personality
Responsibility Orientation” model. This encompasses both external characteristics of
the learning process (i.e. phases of self-directed learning) and internal, personal traits
of learners who engage in self-directed learning. Due to its primary interest in
pedagogy and students’ agency, this study sees self-directed learning as an overarching
concept that includes both external and internal characteristics.
According to van Meeuwen et al. (2013), self-directed learners should have the
capability to:
1. Formulate their own learning needs which specify discrepancies between
their actual level of knowledge and performance and the desired knowledge
and performance as specified in final attainment levels (typically imposed
by an organisation);
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2. Set learning goals which gradually steer their learning process towards the
final attainment levels; and
3. Select those learning tasks (e.g., tasks for practice, study of resources,
asking for feedback) that best help them to reach their learning goals. (p.
194)
Based on this characterisation of self-directed learning, researchers have defined the
following four phases of the process (Brand-Gruwel et al., 2014; Neelen & Kirschner,
2017): 1) determine learning and performance needs; 2) determine learning and
performance objectives; 3) determine learning materials and/or (performance support)
tools; and 4) orient, plan, monitor/reflect, assess/evaluate.
Apart from looking at the ways of assisting learners to further develop their
internal personality traits naturally, and thereby making them self-directed learners
(Merriam, 2001), research also emphasises that self-directed learning nurtures
“emancipatory learning and social action” (Rana, Ardichvili, & Polesello, 2016, p.
473), and hence social, collaborative, and cooperative learning. Self-directed learning
is essentially a learner-driven as well as highly social activity, with the environment
impacting on the learner’s control over their own learning process (Rana, Ardichvili
& Polesello, 2016). In fact, it has been argued that self-direction results from
interaction between learners and their environment (Candy, 1991), and thus, self-
direction can be taught and learned. Such a socio-cognitive perspective highlights the
reciprocal role of the interaction between the environment and the individual
(Bronfenbrenner, 1977, 1986, 1994, 2005; Bronfenbrenner & Morris, 2006; Fettes,
2003), and also makes salient the socio-cultural aspect of self-direction (Vygotsky,
1978), as it can be developed through interaction with more advanced peers. Numerous
researchers agree that self-directed learning skills should be explicitly taught to
professionals, especially those working in complex environments (Bolhuis, 2003;
Rana, Ardichvili & Polesello, 2016; Van Meeuwen et al., 2013).
Furthermore, researchers (Boyer et al., 2013; Rana, Ardichvili & Polesello,
2016) also argue that the teaching of self-directed learning skills should commence in
the formal, institutional settings of school or university, and educational institutions
“should seek to create learning environments and conditions that facilitate learners’
self-direction” (Rana, Ardichvili & Polesello, 2016, p. 473). For instance, Boyer and
her colleagues (Boyer et al., 2013) who, for the last 30 years, have conducted a meta-
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analysis of research on self-directed learning across five countries and within diverse
disciplines, concluded that self-directed learning practices should form a part of
university curricula.
In fact, specifying requirements for the bachelor’s degree (AQF level 7), the
Australian Qualifications Framework (2013), under the criterion “describing
application of knowledge and skills”, states:
Graduates at this level will apply knowledge and skills to
demonstrate autonomy, well-developed judgement and
responsibility:
in contexts that require self-directed work and learning
within broad parameters to provide specialist advice and functions. (p. 47)
Therefore, there is a strong argument in favour of developing learners’ self-direction
at the university level. However, this is not a simple task, especially when considering
that university students are still learning discipline-related content knowledge, in
conjunction with developing their skills and capabilities. This means that students
would firstly need to learn how to perceive the different types of knowledges and
diverse ways of knowing, or the patterns in the surrounding environment. Next, they
would need to use appropriate strategies (which implies being conscious of the
existence of these powerful and effective strategies) to develop a systematic approach
to their learning. This approach would include: 1) defining which knowledges they
need to acquire, 2) planning the learning procedures (tasks, steps), and 3) developing
and applying self-evaluation of their achievements. Furthermore, literature (Bransford,
Brown & Cocking, 2000; Clark, Nguyen & Sweller, 2006) has provided evidence of
the difference between experienced, or expert, learners and novice learners. The
difference between novice and expert learners and its implications for this study are
discussed in section 2.9.3.
From the perspective of this study, self-direction adds to the complexity of
designing learning tasks, and more broadly, learning experiences. This complexity lies
in the nature of skills, attitudes, and capabilities one should target to enhance/ promote
self-direction. Some students will be naturally inclined for self-direction due to their
personality traits, while some will be less inclined. The challenge, from the viewpoint
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of this study, is to design learning tasks that would use learning networks to carefully
scaffold students towards more agency, responsibility, control, and self-reflection.
Following Boyer and her colleagues (2013), this should be supported by two types of
scaffolding: technological and human. The technological scaffolding should
encompass appropriately balanced resources, technological tools, and selected
literature. The effectiveness of such scaffolding should be evaluated using ACAD
framework (section 2.2.1), especially the concept of set desging. The second type of
scaffolding, provided by other social agents, should include mentorship, collaboration,
and targeted assistance. It is impossible for one educator to design and implement such
rich learning experiences. The design should therefore assist learners in perceiving and
taking up the affordances for learning offered by their PLNs. This could be achieved
by designing epistemic practices for learners that raise awareness of different
knowledges and diverse ways of knowing, and in this way assisting learners with
becoming epistemically fluent. Again, the evaluation of effectiveness of such a design
should be completed through the lens of ACAD, using the concept of social design. In
addition, it is important to observe that the boundaries between formal and informal
learning networks should be blurred, which is a challenge that requires a joint effort
by many specialists to ensure appropriate epistemic design (Goodyear and Carvalho,
2014a, 2016), or appropriate structure of tasks.
As noted above, the relationship between self-directed and self-regulated
learning can be described in terms of macro- and micro-levels (Brand-Gruwel et al.,
2014). This section has focused on the macro-level; the next section discusses self-
regulation at the micro-level.
2.7.2 Self-regulation
This study uses the definition of self-regulated learning as developed by Schunk and
Greene (2018), who define self-regulated learning as consisting of “the ways that
learners systematically activate and sustain their cognitions, motivations, behaviors,
and affects, toward the attainment of their goals” (p. 1). They clarify that self-
regulation of learning focuses on self-regulating the process (of learning, or of
becoming, of taking an identity). Writing from the social-cognitive viewpoint,
Zimmerman (2002) emphasised the key role of an individual’s agency, a proactive
approach when developing self-regulation. According to Zimmerman (2002), self-
regulation is “the self-directive process by which learners transform their mental
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abilities into academic skills” (p. 65). Therefore, this is not a performance skill, but
rather a learning process. Nevertheless, self-regulated performance facilitates and
enables self-regulated learning. This is an important distinction, as some researchers
(Schunk & Greene, 2018; Zimmerman & Schunk, 2011) differentiate between self-
regulation of learning and self-regulation of performance. Self-regulation of learning
focuses on the learning process itself, underpinned by personal motivations,
behaviours and attitudes, especially personal responsibility for learning. Self-regulated
learning may involve an educator’s assistance, but not necessarily. By contrast, self-
regulated performance focuses on the ways learners (self-regulated learners) perform
(i.e. complete a task) under, sometimes challenging, “performance conditions” (Saks
& Leijen, 2014, p. 191).
Such differentiation is important for this study because it scopes the focus of
the research – it is about raising learners’ awareness of the opportunities for learning
provided by the environment, the ways of perceiving them, evaluating their benefit
and value, and consequently making conscious decisions to take them up (or not), and
use them for learning. This is a cyclical, life-long process requiring metacognition
(knowing how one learns), capability to sustain motivation, and an awareness of
appropriate learning strategies to be skilfully used to achieve the goal, i.e. to learn.
This is the core of the process of learning – over a prolonged period of time,
maintaining a suitable level of interest in learning to discover and acquire diverse
knowledges and ways of knowing, and thus to become epistemically fluent.
Zimmerman (2002) identified and described three phases of this cyclical process: 1)
forethought; 2) performance; and 3) self-reflection.
The difficulties to self-regulate, experienced by students participating in this
study turned out to be one of the key elements revealed in the data analysis (sections
4.1, 4.2 and 4.3), the implications of this finding are discussed in section 5.2. For this
reason, Table 2.1 describes in detail the sub-processes constituting self-regulation, and
provides background for discussion (see Chapter 5).
Table 2.1: Phases and sub-processes of self-regulation according to Zimmerman
(2002).
Phases Sub-phasesPhase 1Forethought
Task analysis:
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Goal setting: setting specific goals to be achieved. This is a particularly important sub-process, as goals serve as criteria against which individuals judge their achievement and take strategic decisions about continuing or modifying their practices. Goals can be proximal (short-term) and distal (long-term). Literature suggests that setting up more concrete goals (e.g. related to learning strategies or development of competencies) (Schunk & Greene, 2018; Schunk & Swartz, 1993) or proximal goals (Wigfiled, Klauda & Cambria, 2011) has more benefits than setting up broad goals (e.g. completing a task) (Schunk & Greene, 2018; Schunk & Swartz, 1993) or distant goals (Wigfiled, Klauda & Cambria, 2011).
Strategic planning is the consequence of goal setting, monitoring of goal achievement and conscious adaptation to the observed results.
Self-motivation beliefs:Self-efficacy: “individuals’ beliefs that they can accomplish different activities” (Wigfiled, Klauda & Cambria, 2011, p. 36). Literature reports on a positive correlation between self-efficacy and goal achievement.
Outcome expectations (Zimmerman, 2002)/ Competence perceptions (Wigfiled, Klauda & Cambria, 2011): individuals’ beliefs and expectations on how good they are at different activities. As for self-efficacy, there is a positive correlation between outcome expectations and goal achievement.
Intrinsic interest/ value (Zimmerman, 2002)/ Task value (Neelen & Kirschner, 2018b; Wigfiled, Klauda & Cambria, 2011): valuing tasks “for their own merits” (Zimmerman, 2002, p. 66), these are incentives or reasons for completing specific tasks.
Learning goal orientation: valuing the process of learning for its own merits (Zimmerman, 2002). This decreases over the school years, with increased complexity and difficulty of learning content (Wigfiled, Klauda & Cambria, 2011).
Phase 2Performance
Self-control: application of specific methods and strategies identified in the forethought phase (Zimmerman, 2002). Individuals engage in metacognitive processes that may provide feedback on the effectiveness of the selected strategy. These can be the following (examples taken from different authors):
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Rehearsal, organisation, elaboration of information, delayed gratification, persistence (Wigfiled, Klauda & Cambria, 2011, p. 38).Imagery, self-instruction, attention focusing, task strategies (Zimmerman, 2002, p. 68).Volition strategies, time management, environmental structuring, help-seeking, metacognitive monitoring (Neelen & Kirschner, 2018b).
Self-observation: the process of self-recording personal events or self-experimentation to find out the cause of these events (Zimmerman, 2002, p. 68).
Phase 3Self-reflection
Self-judgment: composed of two major categories, both self-analytical in nature. Self-evaluation: a comparison between individuals’ recently observed performance and previous performance, someone else’s performance, or a standard performance (Zimmerman, 2002).
Causal attribution: individuals’ explanations of the attained outcomes, success or failures (errors). These are particularly important for the self-reflection phase and the forethought phase, as they will influence this preparatory phase as well (Schunk & Zimmerman, 2008).
Self-reaction: research indicates that emotions are critical in sustaining effort to attain goals and self-regulation (Efklides, 2011).“Positive academic emotions facilitate self-regulated learning” (Wigfiled, Klauda & Cambria, 2011, p.41).
Self-satisfaction/ affect: correlated with motivation, that is, increase in self-satisfaction increases motivation, decrease in self-satisfaction decreases motivation. This is similar to a positive affective reaction to achievements. In fact, motivational variables appear to be critical for self-regulation and can impact on individuals’ decisions to continue or abandon attainment of goals (Schunk & Zimmerman, 2008).
Adaptive/ defensive: reactions depend on the goal attainment and individuals’ self-reflection. Adaptive reaction aims to increase the effectiveness of learning (e.g. by improving the strategy or changing it) while defensive reaction results in withdrawal or avoidance (Zimmerman, 2002).
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Becoming a self-regulated learner is important at any stage of an individual’s life and
within any spaces and places where individuals learn, work, and live: in the formal
context of institutional education and in the informal context of private or professional
environments (Zimmerman, 2002). Using Bronfenbrenner’s terminology, this
particular intellectual capability affects all spheres of an individuals’ life. Usher and
Schunk (2018) make an important point regarding the nature and role that self-
regulation plays, and will increasingly play, within the contemporary technology-rich
world of overstimulation and information overdose. According to the researchers, the
ability to self-regulate becomes the key element of a successful, autonomous life
(Usher & Schunk, 2018). This conclusion confirms Zimmerman’s (2002) conclusion
regarding the role self-regulation plays for agentic, autonomous learning: the most
important aspect of learning is the development of an individual’s (a student’s)
capability to self-regulate. Again, the interaction between the individual and their
environment turns decisive when facilitating their process of learning.
In this study, self-regulation provides another layer of challenges when
designing both learning networks and learning tasks for students. How to design a
learning network that would encourage learners’ self-regulation? This would need to
be achieved through the design of a learning network that would make diverse
affordances for self-regulation (i.e., for planning, organising, self-reflecting) salient to
learners. These affordances could be provided in the form of resources, technological
tools, selected literature, but also mentorship activities and specific, targeted support
(Boyer et al., 2013). Learners’ capability to self-reflect appears to be key in enabling
self-regulation. Hence, learning tasks should be designed to assist students with
recognising patterns, deeply reflecting on them, realising the value of different
knowledges and diverse ways of knowing embedded in the surrounding environment
(patterns), and benefitting from this by taking advantage of educational affordances.
Again, to be able to create such a design, educators would need to have an appropriate
level of epistemic fluency, especially in the domain of learning design. This includes
understanding the important difference between novice and expert learners, as
discussed below.
2.7.3 Novice vs expert
Research within educational psychology indicates that learners perceive their
surrounding reality, depending on their cognitive stage (Piaget, 1955) or the level of
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“approximation of reality” (Kirschner, 2009, p. 146); or, in other words, depending on
their positioning on the spectrum from novices to experts. According to Kirschner,
(2009), novice learners “apply inadequate, often faulty novice theories that differ
greatly from the sophisticated theories of a domain or the world held by practitioners”
(p. 146). This is due to the human process of cognition, especially by the ways the
human brain functions. Research within the sciences of learning (Bransford et al.,
2000; Kirschner, 2009; Van Merriënboer & Sweller, 2005) describes human learning
as a process of acquiring new information by encoding information provided by the
environment, retaining it in one’s short-term memory, storing the information in one’s
long-term memory in the form of cognitive schemata that vary in the degree of their
complexity, and then retrieving it to generate a representation or produce a learned
behavior (Bransford et. al., 2000; Van Merriënboer & Sweller, 2005). Researchers
within the learning sciences have identified six interconnected principles of experts’
knowledge (Bransford et al., 2000; Kirschner, 2009; Van Merriënboer & Sweller,
2005):
1. Meaningful patterns of information – Experts have superior ability to
acquire, store, and quickly retrieve cognitive schemata in their long-term
memory, due to their well-developed capability of using a chunking
strategy.
2. Organisation of knowledge – Experts meaningfully organise their
knowledge around key concepts or important ideas. They recognise patterns
of concepts and make connections between them.
3. Context and access to knowledge – Experts’ knowledge is deep and broad
at the same time. The deep knowledge is specialised and conditionalised,
that is experts are able to retrieve appropriate knowledge in appropriate
contexts. However, experts also possess enough broad knowledge to allow
them to make connections between concepts and big ideas.
4. Fluent retrieval – Experts retrieve relevant knowledge fluently and
automatically, not faster than novices. Quite the opposite, experts often
spend more time on paying conscious attention to detail instead of providing
quick solutions.
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5. Content expertise and teaching – Content experts are usually not skilful
teachers, because they tend to forget about the difficulties they had to
overcome in becoming experts.
6. Adaptive expertise – Adaptive experts adapt to the environment and search
for creative solutions. They also have well-developed metacognitive skills,
that is, they have the ability “to monitor their current level of understanding
and decide when it is not adequate” (Bransford et al., 2000, p. 47).
From the perspective of this study, it becomes evident that with learners’
progression through cognitive stages from novice to expert, learning design will need
to adjust, to design effective, meaningful, and also concerns the design of formal
learning networks and the ways of expanding those networks into informal learning
networks, often used by skilful professionals. This, in turn, requires appropriate
scaffolding while developing learners’ self-regulation and self-direction.
As Zimmerman (2002) observed, self-regulation can be successfully taught,
resulting in an increase in students’ motivation and overall achievement (Schunk &
Zimmerman, 1998; Zimmerman, 2002). The question is “how?” Research suggests
that novice learners (i.e., those who need assistance in developing their metacognitive
capabilities to properly self-regulate) approach the development of self-regulation
skills differently than expert learners (i.e., those who take conscious control of their
learning practice) (Cleary & Zimmerman, 2000; Zimmerman, 2002). That is, novices
tend to focalise their attention and efforts on reactive strategies such as for example
comparing themselves with others. As others usually improve their performance as
well, the reactive strategy of novices results in continuously benchmarking criteria for
success, which is hard to achieve. When failing, or not progressing as expected,
novices tend to attribute their low achievement (or lack of achievement) to their low
abilities, which, in turn, strongly de-motivates them and often results in defensive
attitudes such as withdrawal from learning. By contrast, experts set hierarchical,
developmental goals, which are well-structured and can be successively broken down
into smaller, easier to achieve goals. This type of learner tends to plan their actions in
advance, using powerful strategies such as “a visual organiser for filling in key
information (Zimmerman, 2002, p. 69), evaluation of self (as opposed to comparisons),
and flexibly adjusting by developing new strategies (as opposed to attributing low
achievements to personal abilities). Thus, the difference between novice and expert
92 Literature Review
learners should be considered when designing learning experiences for students. In
practical terms, this means better use of personalised learning networks and the
pedagogical approaches they offer, through for example learning analytics, to collect
more detailed, specific and targeted information about learners.
It is important to reiterate that self-direction and self-regulation can be taught
and, therefore, learnt (Bjork, Dunlosky & Kornell, 2013; Brand-Gruwel et al., 2014;
Neelen & Kirschner, 2018; Zimmerman, 2002). All the above-mentioned arguments
support calls for assisting students in developing their self-direction and self-
regulation. That is, learning networks should be designed in ways to make affordances
for self-directed learning salient to students (Boyer et al., 2013). Moreover, formal
learning networks should be designed in ways to encourage students to make
connections with their informal learning networks, which would promote self-directed
learning.
As for complex learning tasks, they should be designed in ways to encourage
students’ epistemic practices. Tasks should assist students in perceiving the
affordances for self-directed learning, realising different knowledges and ways of
knowing, recognising patterns, and consciously using the knowledge of patterns to co-
construct new knowledge.
Research indicates that antecedents to self-directed learning, such as support,
motivation, self-efficacy or locus of control (Boyer et al., 2013), if well exploited by
educators, can positively impact on students’ self-direction. Hence, the design of
learning tasks should skilfully use learning networks to capitalise on the above-
mentioned antecedents. However, for these antecedents to be effectively exploited, the
students should be carefully scaffolded through the process, to avoid overloading their
cognition, which would negatively impact on their learning, motivation, and
willingness to perform the task (Van Merriënboer et al., 2003).
Following calls from researchers (Bjork, Dunlosky, & Kornell, 2013; Neelen
& Kirschner, 2018; Zimmerman, 2002), learning networks and learning experiences
designed for these networks could promote development of learners’ capabilities and
skills in planning, setting up goals and self-reflecting, and hence self-regulation. This
should be supported/ initiated by activities that assist learners in developing their
critical views on their own perceptions of their learning performance, especially self-
efficacy in learning. Such a critical lens would serve as a starting point for self-
Literature Review 93
reflection on learners’ current achievements, envisaged goals, and planned activities
to attain their next goals.
Furthermore, Bjork, Dunlosky and Kornell (2013) argue in favour of explicit
teaching how to monitor and control one’s learning activities. Neelen and Kirschner
(2018) note that this could be done by, for example, teaching learners how to
“continuously assess themselves and how to keep the balance between monitoring and
control” (n. p.). This means designing learning networks that would contain explicit
information about powerful and effective learning strategies.
Such formal learning networks should be part of a flexible learning
environment that combines face-to-face scaffolding, with relevant resources to support
acquisition of development of self-regulation and self-direction (Brand-Gruwel et al.,
2014; Van Merriënboer & Kirschner, 2017), because these skills do not develop
spontaneously (Neelen & Kirschner, 2018a).
Designing to promote self-directed and self-regulated learning is itself a
complex activity, demanding specialist knowledge, and not all educators have the
relevant level of knowledge, nor are appropriately trained and experienced to
undertake such responsibility. Hence, opportunities for learning about self-regulation
and self-direction should be offered to educators themselves. For instance, Neelen and
Kirschner (2018) recommend that educators should better understand how human
memory works. This knowledge would provide appropriate epistemic foundations for
designing effective and appropriately balanced learning tasks for students. The tasks
that would be designed, would then consider learners’ cognitive load (Sweller, 1988;
Kirschner, Sweller & Clarke, 2006) and their current cognitive stage, positioning them
somewhere on the spectrum between novice and experts (Bransford et al., 2000; Van
Merriënboer & Sweller, 2005; Kirschner, 2009).
All the above clearly points towards the importance of investigating learning
networks and networked learning from the perspective of learning design, with sound
foundations in educational psychology offering research-informed and research-based
effective learning strategies.
2.8 REFLECTION ON LITERATURE
This chapter has systematically discussed the ontological research paradigm and
epistemological underpinnings of the study.
94 Literature Review
The ontological perspectives on the ecology of human learning influenced the
choice of relevant theories and frameworks, which are all diverse yet all have, at their
centre, a vison of human learning as a social, collaborative and technologically
mediated (connected) activity of acquiring knowledges, de/re-/co-constructing them
and creating new knowledges and sharing them with others.
The sections presenting epistemology of the study have made salient the
difficulty of describing connected learning within learning networks. These are
complex activities, requiring certain levels of awareness of self-regulation and self-
direction from network users (practitioners). The challenge with embedding learning
networks into the educational landscape and enabling connected learning lies, to some
extent, with the fact that many everyday users of networks do not necessarily perceive
learning affordances provided by networks and/or they do not associate networks with
the process of learning. Moreover, this challenge is augmented by the requirement of
self-awareness that would assist users of networks in becoming networked learning
practitioners. In short, networked learning implies self-regulation and self-direction,
the attributes of a reflexive, self-aware learner. The ACAD framework was included
as it provided a background for interpretation of the findings and developing the final
outcome that emerged from the study.
The common thread across the epistemological underpinnings of this study is
therefore the problem of assisting learners in perceiving the learning affordances in the
environment and, using the principle of reciprocity (Bronfenbrenner 1977, 1986, 1994,
2005; Bronfenbrenner & Morris, 2006; Fettes, 2003), design networks in such a way
that they would make learning affordances salient to the network’s users, turning them
into networked learning practitioners in the process. Considering this difficulty, this
doctoral dissertation investigates presupposed existence of two learning networks: 1)
institutionally designed (named formal) and non-institutionally created by students,
private (named informal).
The concept of presupposition covers a broad range of semantic and pragmatic
phenomena that have important consequences for understanding of utterances. In
semantics, presuppositions are “propositions whose truth is taken for granted in an
utterance and without which the utterance cannot be assigned a truth value”
(Marmaridou, 2009, p. 349). That is, the presupposition is valid even when the
utterance is negated. As the presuppositions are sensitive to the overall knowledge of
the world, they are also objects of study in pragmatics. Put simply, but not
Literature Review 95
simplistically, one’s overall knowledge of the world may play the role of a
presupposition trigger, a form of precondition that implies the existence of the
presupposition. Dekker (2012) explains:
A presupposition is not so much the ground you stand on, or the
thinking that you rely on, but more like what comes before the
ground you stand on, or what comes before the thinking that you rely
on. The notion of presupposition is therefore in essence quite a
philosophical subject. By the same token, presuppositions are things
which one can try to communicate, without actually stating them.
(p.42)
Thus, one’s frame of reference and the knowledge of functional context should be
considered the ground for presupposing existence of learning networks, and, in
consequence, their architecture. That is, when designing the study, based on her frame
of reference (i.e. being a researcher, an academic teacher), her knowledge of the
learning networks (doctoral student researching the learning networks) and the
functional context of the courses under investigation, the Author “supposed before”
(Dekker, 2012, p.42), or pre-supposed: 1) the existence of at least two types of learning
networks, ad 2) their probable composition, in terms of constituting elements,
especially in the case of formal learning networks.
The argument of presupposing the existence of learning networks is in fact consistent
with the ideas of Goodyear and Carvalho of learning networks. That is, Goodyear
(2000) observed:
Our use of the word ‘design’ encompasses some of the activities
undertaken by teachers when they are preparing for teaching – a part
of educational work that we have previously labeled ‘teaching-as-
design’ (Goodyear and Ellis 2007, Goodyear and Retalis 2010). This
does not just embrace lesson planning or the preparation of lectures,
but all aspects of design for learning, including those activities that
result in the creation of learning places (physical, digital and hybrid),
learning tools and resources, learning tasks and the social
organization of learning arrangements, proposed divisions of labor,
etc. (p. 16).
96 Literature Review
Such a focus on “designing for learning” leads to a conscious, intentional design, but
also implies a ground knowledge, an idea of the object that is being designed. In this
context, the object that is being designed are learning networks.
In summary, the section on learning networks introduced the concept and
showed the richness of ideas around the notion of learning networks and their role in
formal and informal learning and showed the ecological connection between this
notion and the notion of affordances.
The next sections on connectivism and connected learning in the context of HE
presented the same problem from a slightly different angle, namely around what
constitutes knowledge within networks, and how to design learning experiences that
would encourage the uptake of distributed knowledge and its co-configuration and co-
creation.
At this point, one observation needs to be made regarding the connenctivist perspective
on learning/ knowledge acquisition. As demonstrated above, connectivist accounts on
learning use the term “knowledge consumption” (Kop & Hill, 2008, p. 2) to describe
learners’ (rather) passive attitude towards learning within a network (Downes, 2010a).
From the connectivist viewpoint, it is acceptable to use such a metaphor to emphasise
learners’ low levels of engagement with knowledge (i.e., the content of the network),
especially in the process of sharing knowledge through networks. From the socio-
constructivist perspective, such a metaphor presents challenges in itself regarding the
nature of learning. Constructivism is a theory of cognition (Kirschner, 1992, 2009) that
sees learning as an active de-, re-, and co-construction of knowledge. Learners actively
construct their own cognitive schematas that can be developed by adding new
schematas and storing them in long-term memory. Complemented by social
perspectives that emphasises the role of the other in learning, socio-constructivism
sees learning as a de-, re-, co-construction of knowledge, a process that occurs in
collaboration with less-, equally- or more-advanced peers (Vygotsky, 1978; Piaget,
1959; Bruner, 1966, 1990). The socio-constructivist accounts on learning point to the
social, collaborative aspect of the learning process. In educational situations, an
important part of knowledge is co-constructed, through interactions with others. Even
if the interaction appears limited (e.g. listening to a lecture), the process does occur.
Thus, from both perspectives, the cognition or the transfer of knowledge is not direct,
it is mediated by network (connectivism) or by co-construction of schemata (socio-
constructivism). This has important implications for the pedagogical approaches of
Literature Review 97
both theories, including the langue and expressions used to describe the learning
process.
The term “knowledge consumption” used by connectivist accounts, can appear at least
misleading from the socio-constructivist viewpoint. On one hand, consumption implies
the act of taking from, appropriation and excludes co-creation. On the other hand,
socio-constructivism implies taking from and building, or constructing. The two
viewpoints, although attempting to describe the same process of cognition, have
different foci. While connectivism focuses on network (external) and the interactions
between the nodes (i.e. constituting elements) in the network, and in this way learning,
socio-constructivism focuses on construction of schemata (internal) in one’s brain,
resulting in learning as well. Recent advances in research in neuroscience have pointed
towards an important overlap between the two viewpoints. The social brain hypothesis
(Miller, 2011):
posits that the increasing complexity of social networks in primates
in general and humans in particular drove the expansion of the
cerebral cortex, or at least certain parts of it. (p.578)
It appears to be a correlation between the anatomy of human brain and the size of a
social network. In other words, human (primates) ability to effectively co-construct
social networks might promote the evolution of brain, and increase the capability of
social cognition (Miller, 2011).
From the perspective of networked learning, it is crucial therefore to
appropriately capture the important role of knowledge sharing and feeding it back to
network. The challenge is to describe this phenomenon, and the term consumption is
deficient from this perspective. For this reason, except in the direct quotations, this
study will use the proposed term knowledge assimilation to illustrate the above-
discussed process of learning within the network but not engaging in the interaction
within it.
Finally, the section on active learning has pointed towards the somehow
conflicting requirement of having high awareness levels of self-regulation and self-
direction to use learning networks productively, even at the stage of being a novice
learner.
98 Literature Review
All these elements together have informed the development of the final
outcome that emerged from this study: an ecological curriculum and learning design
framework of connected epistemic domains.
Research design 99
Research design
This chapter offers a detailed description of the selected research methodology and
research methods. Section 3.2 presents the methodological framework, including the
rationale for using it, overview of its principles and an explanation of how the
framework was used in this study. The research methods (section 3.2) ensue from the
chosen methodological framework, and support the investigation of the research
questions. A mixed methods approach was adopted and a rationale for this choice is
provided in section 3.2.1. The chapter closes by discussing data collection techniques
(section 3.2.2), data analysis procedures (section 3.3), the study’s trustworthiness
(section 3.4), and ethical considerations (including appropriate protocols implemented
to address them) (section 3.5). Figure 3.1 illustrates all constituent parts of the research
paradigm of the study.
Figure 3.1: Research paradigm of the study.
The research paradigm uses the metaphors of enabler and operator to describe
the role of its constituting elements. That is, the research methodology functions as an
enabler, setting up frames for the research design while the research methods function
as operators or actual approach(es) used to collect data, complete the analysis, and
interpret the findings.
100 Research design
3.1 RESEARCH METHODOLOGY
This section presents an overview of dominant research methodologies, which then
provides a rationale for selecting a theory-seeking educational case study as the
appropriate research methodology for this study. Next, different potential study
designs are briefly discussed to present a justification for choosing the embedded,
single-case design as the relevant study design framework. Finally, the study design
that was ultimately used is outlined, with different phases described and mapped
against research questions.
3.1.1 Overview of dominant research methodologies
Social sciences literature, including education, distinguishes three broad, overarching
philosophical perspectives on research methodology: 1) critical theory, also known as
a transformative paradigm; 2) positivist, also known as a scientific method; and 3)
interpretivist, also called a constructivist paradigm (Brundrett, & Rhodes, 2013;
Cohen, Manion & Morrison, 2007; Creswell, 2007; Flick, 2009).
Critical theory is essentially interested in studying society with the purpose of
provoking significant changes in its structure, which are then expected to result in a
deep restructuring of the current society (Brundrett, & Rhodes, 2013; Cohen, Manion
and Morrison, 2007; Creswell, 2007; Flick, 2009). As this goes far beyond the aim and
scope of this research project, this perspective was not considered to provide relevant
tools to answer the research questions, and hence to be incompatible with this doctoral
dissertation.
A positivist perspective attempts to describe social reality in an objective way,
uncovering the causes of events, explaining results, and shedding light on their
governing rules and principles. Investigations are conducted by social scientists who
are impartial observers of the surrounding reality (Cohen, Manion & Morrison, 2007).
This philosophical perspective provides foundations for logical, systematic, and
objective descriptions of reality, uncovering underlying causal relationships between
phenomena studied (Brundrett & Rhodes, 2013). Due to the emphasis on causality,
positivism uses quantitative research methods to measure phenomena, draws objective
conclusions and formulates a theory (Brundrett & Rhodes, 2013).
For this study, positivism seemed a promising perspective to adopt to some
extent. Some phenomena needed an objective lens for investigation (e.g., constitution
of students’ learning networks), which in turn required identification, description,
Research design 101
analysis, and generalisation. These research activities are part of the positivist domain,
and, some elements of a positivist approach (i.e., questionnaires and statistical data
analysis) were indeed used in this study. However, despite providing some answers to
the research questions, a positivist approach lacked appropriate tools to identify,
describe, and analyse the full complexity of social phenomena occurring within the
educational settings, and was therefore not fully adopted. However, the principle of
objectivity prompted a further search for an appropriate research methodology that
would still include some positivist principles.
Education requires more subjective research methods that recognise the
dynamic relationship and interactions between social agents and the surrounding
environment. This entails researchers’ involvement in the environments of the social
agents under investigation to identify, describe, and analyse the unique perspectives of
those agents. Such a viewpoint has inspired the development of naturalistic, anti-
positivist research methodologies, one of which is interpretivism (Brundrett, &
Rhodes, 2013; Cohen, Manion & Morrison, 2007; Creswell, 2007; Flick, 2009).
Concerned with the individual, an interpretivist approach places actions taken by social
agents at its centre, which entails a focus on the concepts of human agency, capability,
and skills to plan, monitor, exercise, evaluate, and reflect on one’s own actions. The
aim of the interpretivist lens is to describe the reality from within, at a local scale
(contextualised research), and in this way develop a theory, or a “pattern of meanings”
(Creswell, 2007, p. 9). An interpretivist approach therefore seemed appropriate here,
as this study aimed to gain a deeper understanding of learning networks and networked
learning practices of first year undergraduate students enrolled in STEM-focused units
of study to develop evidence-based, contextualised learning design that has the
potential to effectively assist undergraduate students in becoming epistemically fluent,
lifelong, active learners.
One of the research methodologies that resonates well with interpretive
research methods is the case study (Cohen, Manion & Morrison, 2007). Case studies
provide an in-depth analysis of a specific environment and the particular circumstances
within which analysed events or phenomena take place. Cohen, Manion and Morrison
(2007) characterise the case study as a research methodology in the following way:
A distinguishing feature of case studies is that human systems have
a wholeness or integrity to them rather than being a loose connection
102 Research design
of traits, necessitating in-depth investigation. Further, contexts are
unique and dynamic, hence case studies investigate and report the
complex dynamic and unfolding interactions of events, human
relationships and other factors in a unique instance (p. 253).
According to Hitchcock and Hughes (1995), apart from providing a rich description of
the events, case studies are also used to understand social agents’ perceptions of these
events.
There are many types of case studies (Cohen, Manion & Morrison, 2007) and
their classification is based on their epistemological status (Scholz & Tietje 2002) such
as, for example, exploring, narrating, describing, or evaluating. Yin (2014) has
distinguished three types of case studies: 1) exploratory; 2) descriptive; and 3)
explanatory. Others, such as Struman (1999), identified four types of case studies: 1)
ethnographic; 2) action research; 3) evaluative; and 4) educational case studies. When
reflecting closer on educational case studies, Bassey (1999, p. 12) advanced the
following classification: 1) theory-seeking and theory-testing case study; 2)
storytelling and picture-drawing case study; and 3) evaluative case study.
Considering that the epistemological status of this study is to investigate the
patterns of human learning to develop a theoretical framework, Bassey’s (1999)
classification seemed the most appropriate and relevant to the epistemological status
of the research. For this reason, the research methodology employed in this study is a
theory-seeking educational case study.
Bassey (1999) provided a list of claims recommended for researchers to check
whether their research fulfils the criteria of an educational case study. Table 3.1
presents the claims mapped against the particular features of this study, providing a
rationale for classifying this research as a theory-seeking educational case study.
Table 3.1: Rationale for classification of this research as a theory-seeking educational case study. Adapted from Bassey (1999, p. 20).
Criteria ResponsesWhat kind of an educational case study is this?
A theory-seeking case study, as its ultimate aim was to develop a contextualised learning design framework.
Research design 103
Is this an empirical enquiry conducted within a localised boundary of space and time?
Yes, the study is located within educational settings and data were collected within a limited timeframe.
Does it examine interesting aspects of an educational activity, or programme, or institution, or system?
Yes, it analyses learning networks and networked learning practices from two perspectives: students as social agents acting within their learning networks and academic staff as other social agents perceiving students’ actions.
Is it set mainly in its natural context and with an ethic of respect for persons?
Yes, the case study is located within an educational context and follows identified ethical clearance protocols.
Does it inform the judgements and decisions of practitioners, policy-makers, and/ or theoreticians?
Yes, as the final aim is to develop a contextualised, evidence-based and research underpinned learning design framework.
Were sufficient data collected for the researcher to be able to …
Yes. A questionnaire was responded to by 135 students and 8 academic staff members. Student focus groups were attended by 17 volunteering participants. Academic staff focus groups were attended by 6 volunteering participants.
explore significant features of the case? Yes create plausible interpretations? Yes test for the trustworthiness of these interpretations? Yes construct a worthwhile argument or story? Yes relate the argument to the literature?
Yes
convey convincingly to an audience this argument or story? YesIs there a case record which could provide an audit trail that other researchers could use to validate or challenge the findings, or construct alternative arguments?
Yes. All research data are in the researcher’s possession, and stored according to the protocols of ethical clearance.
In summary, this study used a theory-seeking educational case study
methodology to conduct the research, and to formulate and interpret the findings. It
also enabled the development of an ecological curriculum and learning design
framework of connected epistemic domains that emerged from this study.
104 Research design
Scholz and Tietje (2002) observed that the status of a case study determines its
design. Yin (2014) has distinguished four main case study designs: 1) single-case
design exploring one, critical case; 2) embedded, single case-study design exploring
more than “one unit of analysis” or the actual source of information, that become sub-
units of analysis and are embedded in the design of one case study; 3) multiple case
design where many case studies are explored as separate cases; and 4) embedded
multiple-case design with numerous sub-units of analysis embedded in multiple case
studies. Scholz and Tietje (2002) explained that embedded case studies use more than
one research method, qualitative and quantitative methods, and/or statistical methods
to allow evidence that emerges from the research to be investigated “at least partly in
sub-units, which focus on different salient aspects of the case” (p. 10).
This research, although looking at two separate units of study offered at the
university, explores different aspects of the single overarching research problem of
how to increase learners’ awareness of diverse opportunities for learning offered by a
connected environment of learning networks (formal and informal).
Within this case study, there are two embedded sub-units (Unit E and Unit S),
investigated from the perspective of students and academics, and focusing on salient
aspects of the study (e.g., architecture of learning networks, student self-reported
practices, and their perceptions by academic teaching staff). For the above-outlined
reasons, the design of this study has been described as an embedded, single-case study.
The next section explains in more detail the study design.
3.1.2 The embedded, single-case study design
Nisbet and Watt (1984) recommended the following broad phases when designing a
case study: 1) the initial phase, characterised by a wide focus that would enable many
factors and ideas to be considered; 2) the progressive focusing phase to narrow down
the main focus of study while collecting the data; 3) the data interpretation phase with
draft interpretations to be checked with respondents.
This case study’s design followed the above-mentioned phases. After an initial
phase preparing for data collection (i.e., selection of the data collection techniques,
and design, development and piloting of relevant data collection instruments), the next
phases were designed to collect the data and to formulate findings (data analysis). Data
collection was completed in two phases. First, data were collected from students and
academic teaching staff using structured questionnaires. This phase closed with data
Research design 105
analysis leading to formulation of preliminary findings, informing focus group
questions. Next, data were collected from students and academic teaching staff via
focus groups, which informed preliminary findings from the questionnaire and enabled
final findings to be formulated.
This section has provided the rationale for selecting the theory-seeking
educational case study as the appropriate research methodology and the details of the
study design. The next section discusses research methods, the ways of
operationalising the design, and the collected data to be analysed.
3.2 RESEARCH METHODS
Considering its multilayered and complex nature, this study does not fit into the large
spectrum streaching from quantitative to qualitative methods. The selected
combination of qualitative and quantitative methods offers the potential to
complement each other, taking into consideration the observable phenomena and more
difficult to grasp, subjective accounts. The definition of learning networks, as used in
this study, has been provided in sections 1.3 and 2.4. As learning networks are
assemblages of constituting elements, their investigation requires a quantitative lens
to describe observable phenomena, such as the structure of the networks, the
characteristics of their constituents, and the affordances for learning they offer. Such
a description necessitates objectivity based on facts and measurable evidence. On the
other hand, it also requires a qualitative lens to describe the phenomena occurring
within the networks, the interactions between their constituents, and the ways the
social agents perceive affordances for learning and taking them up (or not). A
qualitative lens provides relevant instruments (e.g., open-ended questions, comments)
in this respect. Thus, this study used mixed research methods to collect, analyse and
interpret the data.
The next section outlines the data collection techniques, the research
participants and the data collection schedule, followed by a description of how the data
were collected, analysed and interpreted. Finally, the section addresses
trustworthiness, ethics and the limitations of the research.
3.2.1 Mixed methods research
The literature offers many definitions of mixed methods research (Creswell & Plano
Clark, 2007; Creswell et al., 2011; Onwuegbuzie et al., 2003; Onwuegbuzie et al.,
106 Research design
2003; Ponce & Pagán-Maldonado; 2015; Tashakkori & Creswell, 2007). Creswell and
Plano Clark (2007) coined the following definition of mixed methods research:
Mixed methods research is a research design with philosophical
assumptions as well as methods of inquiry. As a methodology, it
involves philosophical assumptions that guide the direction of the
collection and analysis of data and the mixture of qualitative and
quantitative data in a single study or series of studies. Its central premise
is that the use of quantitative and qualitative approaches in combination
provides a better understanding of research problems than either
approach alone (p. 5).
Creswell, Klassen, Plano Clark and Clegg Smith (2011, p. 4) have identified
five main characteristics of mixed research methods: 1) a focus on research questions
that call for real-life contextual understandings, multi-level perspectives, and cultural
influences; 2) the use of rigorous quantitative research assessing magnitude and
frequency of constructs, and rigorous qualitative research exploring the meaning and
understanding of the constructs; 3) the use of multiple methods; 4) intentionally
integrating or combining these methods to draw on the strengths of each; and 5)
framing the investigation within philosophical and theoretical positions.
This study displays all these characteristics. With regards to characteristic one,
the research questions stem directly from identified, real-life problems and were aimed
at providing contextualised solutions to be applied in educational settings. Moreover,
the study design allowed for multiple perspectives to be investigated (i.e., learning
networks, networked learning practices), by collecting the data from two diverse
cohorts (with some similarities).
As for the second characteristic, the survey (quantitative method) investigated
not only the construct of affordance offered within the educational environment itself
(i.e., the perception and uptake of various types of affordances), but also looked at the
perceived importance of the affordances, and the frequency and intensity of their
uptake (see section 3.2.4 for details). This information was complemented by an
investigation of students’ perceptions of the importance of a given affordance via
open-ended questions and comments (qualitative method).
Research design 107
The next two characteristics have already been discussed earlier (Section 3.1.2)
and in this section. This study used a combination of quantitative and qualitative
methods, that is, it intentionally blended “structures, or ways of combining” (Ponce &
Pagán-Maldonado, 2015, p. 116) the two approaches. The quantitative method, a
questionnaire, included some open-ended questions and comments that allowed for
extended responses and insightful description of processes occurring within learning
networks (e.g., rationale for students’ practices). These, in fact, were of a qualitative
nature. The questionnaire was later followed by focus groups that used qualitative
research methods. Such a mix of research methods ensured that the advantages of one
method would counterbalance the shortcomings of the other.
The literature provides numerous classifications of mixed methods designs
(Creswell et al., 2003, pp. 169-170), one of which is a sequential explanatory mixed
strategy (quantitative first) with integration of qualitative instruments occurring at the
point of data collection (Creswell et al., 2003, pp. 169-171). In this study, the data were
collected sequentially, in one single study, with priority given to a quantitative
instrument (questionnaire) but with the integration of qualitative instruments (i.e.,
open-ended questions, comments). That is, the data collection, analysis and
formulation of findings were conducted in two phases. In phase one, the questionnaire
containing qualitative instruments was administered to students and academic teaching
staff. After a first layer of data analysis and formulation of preliminary findings, focus
group questions were designed and focus groups held. Figure 3.2 illustrates the
sequential mixed model used in the study.
Figure 3.2: Sequential explanatory mixed strategy (quantitative first) with integrated qualitative instruments.
108 Research design
As the type of research design dictated the data collection techniques, the next
section describes these in more detail.
3.2.2 Data collection techniques
Cohen, Manion and Morrison (2007) observed that data collection techniques should
be considered very carefully, to generate meaningful, rich, and relevant data to inform
the findings. The selection of the data collection techniques should therefore be guided
by the relationship between research questions, associated research function(s), and
relevant research method(s) (Plomp, 2013). The three research questions of this study
had a direct impact on research functions and their consequent, associated research
methods. Table 3.2 below summarises the relationship.
Table 3.2: Relationship between research questions, research functions and research methods.
A second factor to consider while selecting appropriate data collection
techniques is the research participants. As the study focused on identifying and
investigating learners’ learning networks and networked learning practices with the
aim of raising awareness of available learning strategies and developing a
contextualised learning framework to assist students in becoming active learners, the
natural choice of research participants were students and academics. This resulted in
two data sets: structured questionnaires (quantitative data) containing responses to
Research question Research function Research methodRQ1 What learning affordances offered by presupposed learning networks were perceived and taken up by undergraduate STEM students?
To identify Quantitative with elements of qualitative (open-ended questions/ comments)
RQ2: What networked learning practices are occurring within the presupposed learning networks?
To describe Qualitative and quantitative
RQ3: What are academic staff participants’ perceptions of students’ networked learning practices within the presupposed learning networks?
To identifyTo analyse
Qualitative and quantitative
Research design 109
open-ended questions/ (qualitative) comments, and transcribed focus group audio-
recordings (qualitative data).
The next section describes the research participants, including their
demographics, and the recruitment protocols and processes.
3.2.3 Research participants
Given that the current research aims to gain a deeper understanding of learning
networks and networked learning practices of first year undergraduate students
enrolled in mathematics units of study in science and engineering degrees and to
develop a contextualised learning design that would have the potential to effectively
assist undergraduate STEM students in becoming active learners, the two large, first
year Units teaching highly diverse student cohorts, were a natural choice for the study.
Two groups of participants took part in the research: 1) students enrolled in the targeted
units of study7; and 2) academic staff teaching in these units. The demographic
information on research participants provides an important background for
interpretation of data and discussion, especially from the perspective of cohort
diversity understood here as preparedness level in mathematics.
As explained in section 2.4, one of the constituting notions of affordance is
frame of reference, social agents’ identity impacting on their interactions with the
environment. For this reason, the below presented demographic information was
collected as it revealed certain aspects of research participants’ identities, enabling the
interpretation of the findings in the context of higher education. The de-identified
student data were retrieved from university systems, while academic teaching staff
data were collected directly from participants.
3.2.3.1 Research participants: students
The data were collected through paper-based questionnaires distributed to students in
semester 1, 2017, and focus groups held in semester 2, 2017.
With regards to the questionnaire, of the 1165 students enrolled in the two
units, 135 students participated in the study, representing 11.5% of the overall targeted
number of students. In terms of individual units, 75 questionnaires were collected from
Unit E (9% of the 809 students enrolled in the unit in both semesters), and 60
7 To satisfy Ethical Clearance requirements, the units have been de-identified and are named Unit E and Unit S.
110 Research design
questionnaires (17% of 356 students enrolled in the unit in both semesters) from Unit
S. Seventeen students participated in focus groups, with 10 from Unit E and 7 from
Unit S.
The tables below contain the demographic details of students enrolled in both
units in the two semesters.
Table 3.3: Demographic data on gender and international background, Unit E and Unit S.
Unit ESemester
of enrolment
Total number of enrolments
Males Females Domestic International
1, 2017 702 597 105 648 54
2, 2017 107 94 13 93 14
Unit S1, 2017 305 185 120 295 10
2, 2017 51 28 23 45 6
Educational background measured in terms of the diversity of courses studied
by students in both units (either obtained or attempted) is an important element
contributing to the diversity of the student cohort, and Table 3.4 and Table 3.5
summarise this information for both units of study.
Table 3.4: Unit E course enrolment, semesters 1 and 2, 2017.
Unit E Course (Bachelor of…) Semester 1
2017Semester 2
2017Business 1 0Business / Engineering (incl. Honours) 78 0Engineering ( incl. Honours) 443 99Engineering (Honours) / Another degree 141 5Information Technology 1 0Science 32 2Science / Another degree 6 0Creative Industries 0 1
Research design 111
Note: fields separated by “/” indicates study of two degrees simultaneously.
Table 3.5: Unit S: demographic data on students’ course enrolment, semester 1, 2017.
Unit S Course (Bachelor of…) Semester 1
2017Semester 2
2017Applied Science / Laws 1 0Engineering (Honours) / Science 49 0Science 201 50Science / Another degree 54 0Start QUT Program 0 1
Note: fields separated by “/” indicates study of two degrees simultaneously
Finally, Table 3.6 summarises the diversity of students in terms of their highest
level of qualification.
Six student focus groups were conducted: four with Unit E students, each with
two to three participants, and two with Unit S students composed of two and five
students respectively. The groups consisted predominantly of male students, with only
three female students (one enrolled in Unit E and two in Unit S). Out of 17 students
who took part in the activity, there were three mature age students, all enrolled in Unit
E, and one high school student, also enrolled in Unit E and completing a program that
allows high school students to enrol in selected unit(s) prior to finishing secondary
education. Fourteen of 17 students were domestic and all students were studying full-
time.
The above-presented demographic data illustrated the high diversity of
students’ frames of reference for those enrolled in the two selected Units. The diversity
and its impact on students’ ways of perceiving affordances, taking them up (or not),
and interacting with learning networks is presented in Chapter 4 (Findings), discussed
in Chapter 5 (Discussion), and it plays an important part in the conclusions formulated
in Chapter 6.
The next section describes demographic data of academic teaching staff
participating in the study.
112
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Research design 113
3.2.3.2 Research participants: academic teaching staff
Academic teaching staff participants were the academics coordinating and lecturing in
the units, and sessional staff who tutored and/or marked assessments. Unit
Coordinators and lecturers were experienced researchers within their mathematical
disciplines with proven records of achievement in university teaching. The sessional
staff cohort was more diverse in terms of their highest education level. Over the two
semesters, 29 academic teachers were involved in lecturing and teaching in both units.
In Unit E, apart from the two lecturers, both with PhD qualifications, there were 12
sessional academics, including one PhD qualified and one in the process of completing
PhD qualifications, one Honours graduate and nine graduates. Unit S was taught by,
15 academics in total, over two semesters. Apart from one Unit Coordinator, a lecturer
with a completed PhD, the 11 tutors were predominantly recruited from amongst final-
year, high achieving mathematics undergraduates.
Of the 29 academic teaching staff in both units, eight provided responses to the
questionnaire and six took part in focus groups. Three focus groups with academic
teaching staff were conducted over one month. In both units, the focus groups were
composed of one academic teaching staff member, the Unit Coordinator, and two
tutors. Of the six academic teaching staff who participated in the focus groups, three
were female and three male.
There were important differences in participants’ demographics and their
teaching experience. While the group of participants from Unit S was composed of
one Unit Coordinator and two tutors still in their final years of undergraduate studies,
the participants from Unit E were all of international background, with their PhD
completed or being close to completion.
Moreover, the teaching and learning design experience levels also varied. The
Unit Coordinator of Unit S was an experienced academic who had five years of
teaching experience in both mathematics and statistics, and who had also participated
in designing the unit from its beginnings and had taught the unit continuously for the
last five years. In contrast, tutors in Unit S had limited experience, with one participant
having tutored for the last three years and another one just starting to tutor. Participants
in Unit E were well-experienced academics who had between three and 10 years-
experience in lecturing in different countries. None of them, however, was involved in
114 Research design
either the original design of the unit, nor in making modifications in design based on
the unit’s previous evaluations.
3.2.4 Data collection instruments
As mentioned in section 3.3.1, data were collected in two phases. In phase one,
structured questionnaires were used due to their potential to generate measurable data
that could be statistically treated to reveal recurring patterns, regularities, and
relationships. In phase two, focus groups were held with students and academic
teaching staff members. This section discusses the design, purpose and content of the
questionnaire first, and then of the focus groups. Appendix A contains a copy of each
questionnaire and Appendix B copies of focus group questions.
3.2.4.1 Questionnaire
A well-designed structured questionnaire offers the opportunity to elicit data that
provide the researcher with relevant information about concept(s) under investigation,
enables comparisons between samples, and provides direct answers to research
questions (Cohen, Manion & Morrison, 2007). In fact, one key benefit of collecting
direct answers using a structured questionnaire is to clarify and fine-tune research
questions (and the underpinning concepts and constructs) (Creswell, Klassen, Plano
Clark & Clegg Smith, 2011; Wilson & McLean, 1994). Considering the above, as
recommended in the literature (Cohen, Manion & Morrison, 2007), the questionnaire
used quantitative and qualitative question types to systematically examine the
educational environment and to assist with the exploration of its multidimensional
nature.
3.2.4.1.1 Questionnaire design
As the study participants consisted of students and university academic
teaching staff, there were two versions of the questionnaire, each modified and adapted
to suit the intended respondents. The two versions of the questionnaire were, however,
the same in their design (i.e., structured questionnaire, similar type of questions used),
and complementary in nature, as they enquired about the same phenomena, but from
different perspectives.
The student questionnaire was comprised of three parts, each using research-
informed questions based on research in the field of affordances (Czaplinski, 2012),
Research design 115
student engagement (Krause & Coates, 2008), students’ academic self-efficacy
(O’Sullivan, 2011), and ways of measuring achievement goals (Elliot & Murayama,
2008).
The first two parts looked at the respondents’ learning networks and networked
learning practices. As a clear understanding of the concepts and constructs under
investigation enables identification of “the kinds of data required to give the researcher
relevant evidence about the concepts or constructs, e.g., their presence, their intensity,
their main features and dimensions, their key elements” (Cohen, Manion & Morrison,
2007, p. 319), the questions in this part were built around three underpinning concepts
of learning networks and networked learning discussed in detail in Section 2.4:
assemblage, affordances, and agency (Goodyear & Carvalho, 2014a, 2014b, 2016;
Goodyear, Carvalho and Dohn, 2016; Jones, 2015).
The first part of the questionnaire aimed to investigate self-reported
respondents’ claims of their perceptions and uptake of affordances offered by learning
networks. This part of the questionnaire used predominantly dichotomous questions
(yes/no), five-point Likert scale questions with soft response categories (Gonyea,
2005, p. 79) (e.g., strongly agree, agree, strongly disagree), restrictive rating scale
questions (Hoskin, 2012) (e.g., from 1 – 7 and from 1-14), and open-ended questions
(e.g., please explain). To avoid different forms of respondents’ bias, such as a mood-
set, mind-set (Cohen, Manion & Morrison, 2007, p. 336), or dishonest responses,
social desirability effect or halo error (Gonyea 2005; Bowman 2011), the
questionnaire was designed using “overall sequencing structure” (Cohen, Manion &
Morrison, 2007, p. 337) (for more detail on the respondent bias see section 6.3,
Limitations). That is, initial questions were used to gather facts and establish, as
objectively as possible, which affordances were perceived by the respondents and
which were not and why, using either dichotomous questions or five-point Likert scale
questions. Next, restrictive rating scale questions looked at more nuanced information
to find out the degree of importance, frequency and intensity of use of each individual
affordance. The four values intended to measure the degree of significance
(importance), the rate of uptake (frequency), the depth of engagement with (intensity)
(i.e. investment in), and the sequence of uptake (when relevant) of the learning
affordances under investigation. Finally, moving to more personal data, open-ended
questions provided an additional opportunity for the respondents to clarify their
answers and to express their opinions about the affordances under investigation.
116 Research design
The second part of the questionnaire, looking at networked learning practices,
was more descriptive in nature and investigated more subjective information. For this
reason, this part of the questionnaire used more open-ended questions, multiple choice
questions and five-point Likert scale questions. The design of this part was also
sequential, first enquiring about more objective phenomena using multiple choice
questions and five-point Likert scale questions, and next using open-ended questions
to investigate more subjective matters.
The third part of the questionnaire focused on respondents’ demographics (e.g.,
self-reported background information) and student engagement with extra-curricular
activities, and therefore investigated non-threatening questions that respondents could
readily answer (Cohen, Manion & Morrison, 2007, p. 337). The purpose of these
questions was to provide an insight into students’ frames of reference and to enable
interpretation of the role frames of reference played in students’ interactions with
different constituents of their ecological systems (Bronfenbrenner, 1977; 1986; 1994;
2005). The questionnaire used open-ended questions, multiple choice questions, five-
point Likert scale questions, and dichotomous questions that provided insight into
respondents’ frames of reference. The question design was based on published
research on student engagement (Krause & Coates, 2008), student academic self-
efficacy (O’Sullivan, 2011), and on ways of measuring achievement goals (Elliot &
Murayama, 2008). The questions used in these parts were adapted to the needs of the
current study, that is, they expanded on previous research and included the researcher’s
original work. In addition, these questions, as they were not as crucial for the analysis
of learning affordances as the previous series of questions, were placed at the end of
the document, in case of questionnaire fatigue. Table 3.7 illustrates examples of
questions from the student questionnaire.
Table 3.7: Example of questions in the structured questionnaire (student version).
Type of question Sample questionsDichotomous You are: domestic student international student
the first in the family to attend the University
Yes No Five-point Likert scale You attended weekly lectures:
Research design 117
Always/Most of the time/ Sometimes/ Occasionally/ Never
Rating scale So far, you feel you took advantage of these opportunities. Please rank them in ascending order of intensity, from 1 to 7.
Open-ended If you did not attend weekly lectures, please explain why.
A version of the student questionnaire was adapted for use with academic
teaching staff. The student questionnaire was modified to collect data that enabled
comparison between self-reported students’ perceptions and academics’ perceptions
of students’ activities. In this way, potential discrepancies/ correlations were
identified, providing important information for learning design about perception and
uptake of affordances for learning.
The academic teaching staff questionnaire used the same “overall sequencing
structure” (Cohen, Manion & Morrison, 2007, p. 337) and question types as the student
version. Such a design of the questionnaire was aimed at finding out if there was a
relationship between student and staff perceptions of the same affordances (e.g.
complementary, convergent, or divergent perceptions). Thus, where the student
version of the questionnaire enquired about student perceptions of the affordances
offered by lectures (question four), the academic teaching staff version asked about
staff perceptions of the uptake of these affordances by students (e.g., by asking about
estimated size of the cohort attending lectures). In this way the students and academic
teaching staff members’ perceptions of the uptake of affordances were contrasted.
Apart from the advantage of consistency when using the same questionnaire
type and structure as the students’ version, the second advantage, equally important,
was the quality assurance of the study. The variety of question types was part of a
broader strategy of various levels of triangulation of the data (section 3.3). Table 3.8
illustrates examples of questions used in the academic teaching team questionnaire.
Table 3.8: Example of questions in the structured questionnaire (academic version).
Type of question Sample questions
Dichotomous Do you think your students are well engaged with their learning?
Yes No
118 Research design
Five-point Likert
scale
In your opinion, approximately what percentage of your students
attended weekly lectures?
Always/Most of the time/ Sometimes/ Occasionally/ Never
Rating scale In your opinion, what were the opportunities for learning offered by
the weekly lectures? Please check as many boxes as appropriate. Please
rank them in ascending order of importance from 1 to 7.
Open-ended In your opinion, the cohort varied from one week to another, or there
were always the same students who attended lectures?
What was the reason they attended the weekly lectures (even
occasionally)? Any ideas?
Cohen, Manion and Morrison (2007, p. 337) note that piloting is paramount to
questionnaire success. Piloting significantly increases reliability, validity and
practicability of the questionnaire, by improving its design, ensuring the purpose is
clear and the overall quality of the data collection instrument. Following
recommendations from Cohen, Manion and Morrison (2007), both versions of the
questionnaire were trialled and submitted for feedback by three volunteering
academics, all lecturing a STEM-focused discipline, and six students enrolled in
STEM-focused degrees. All volunteers had existing professional relationships with the
researcher. Prior to providing the final version of the questionnaire to the supervisory
team for final approval, volunteering participants completed the questionnaire, with
instructions indicating important aspects to comment on during the pilot. The
instructions included layout and itemisation of the questionnaire, clarity of
instructions, formulation of questions, and length of the questionnaire. The feedback
was provided directly to the researcher during two separate meetings, one with the
students and one with the academics. As a result of the feedback provided, the sections
explaining the purpose of each part of the questionnaire were added, and some
questions and instructions were reformulated for clarity. The piloting team pointed
towards the length of the questionnaire causing questionnaire fatigue as an important
element having a potential impact on the quality of collected data. The changes
implemented as a result of the feedback consisted of redesigning the questionnaire’s
layout to make it look “attractive and interesting” (Cohen, Manion & Morrison, 2007,
p. 338) for respondents.
Research design 119
In addition, and independently of the piloting activity, consultations with
academic teaching staff members (lecturers, Units Coordinators) were also held. The
quality assurance of the current research is discussed in detail in section 3.3
(Trustworthiness of the study).
3.2.4.2 Focus groups
In phase two, focus groups were conducted for an in-depth, targeted data collection
enabling the preliminary findings formulated in phase one to be fine-tuned and final
findings to be made.
Focus groups are composed of “the people who possess certain characteristics
and provide qualitative data in a focused discussion to help understand the topic of
interest” (Krueger & Casey, 2000, p. 10). Considering the guidelines from the research
(Cohen, Manion & Morrison, 2007; Saunders, Lewis & Thornhill, 2009), the focus
groups in this study were carefully planned to gain a deeper insight into respondents’
learning networks and networked learning practices, and to refine some points needing
clarification that emerged from preliminary data analysis. The next section discusses
the focus group design in detail.
3.2.4.2.1 Focus group design
Exploratory, semi-structured questions, as recommended in the literature (Saunders,
Lewis & Thornhill, 2009), offered an appropriate format to facilitate an understanding
of “the relationships between variables, such as those revealed from a descriptive
study” (2009, p. 323). Two versions of focus group questions were devised for the two
groups of targeted respondents: students and academic teaching teams.
As recommended in the literature (Cohen, Manion & Morrison, 2007), the
open-ended questions were formulated in plain language avoiding jargon, were printed
out and distributed to participants prior to commencing the activity. The questions also
contained a short, introductory paragraph reminding the participants about the purpose
of the activity and contained prompts to facilitate the discussion. The focus of the
questions drew from preliminary questionnaire findings and were specifically
designed to seek clarifications and / solicit respondents’ more in-depth opinions about
the emerging key findings of the study.
120 Research design
The student focus group questions started by investigating respondents’ frames
of reference (e.g., “Why do you study Engineering/ Science?”). Next, the questions
focused on enquiring about respondents’ learning practices (e.g., “Do you apply any
particular techniques helping you with learning? If yes, what are they, please
describe”). The questions closed with an open invitation for final suggestions or
comments about respondents’ learning experiences in general.
The academic staff focus group questions similarly focused on preliminary
findings from staff questionnaires. The questions started by investigating respondents’
frames of reference and then focused on academics’ perceptions of students’ learning
practices. The last question invited participants to provide suggestions or comments
related to the topic of the focus groups. Table 3.9 illustrates questions for both targeted
groups.
Table 3.9: Focus group questions for students and academic teaching teams.
Sample questionsStudents Why do you study Engineering/ Science?
How good, in your opinion, are you at learning, especially learning mathematics in this unit?When asking for help, do you focus on the assessment? On understanding? On both (you adjust your strategy)? How do you know that the person you are asking for help is reliable, trustworthy?What do you do if you are not interested in the topic? How would you improve your learning? What changes would you like to see implemented in the units you take (in terms of teaching the units)? What changes do you think you should implement in your own learning strategies?
Academic teaching teams
How good, in your opinion, are they at learning, especially learning mathematics in this unit?What do you believe effective strategies for learning are? How should students learn?When stuck, what should they do? Who should they ask for help?What should they do if they would be not interested in the topic?How should they improve their learning? What changes should be implemented in the unit you teach? What changes do you think you should implement in your own teaching methods?
As a part of quality assurance, all aspects of both data collection instruments
(i.e., structure of the questionnaires, types of questions, versions, and data collection
Research design 121
details) were carefully planned. In addition, the questions were piloted with the same
volunteers used for the questionnaires. This activity resulted in questions being refined
to improve their clarity and to ensure they were focusing on the key preliminary
findings from the questionnaire data analysis.
This section has discussed in detail the two data collection techniques used in
the study, namely questionnaires and focus groups. The next section discusses data
analysis techniques and procedures.
3.3 DATA ANALYSIS
As this study used exploratory sequential design as part of mixed methods research
governing data collection and analysis procedures, the data analysis was also
completed in two stages. The investigation and analysis of respondents’ perceptions
and their claims led to the identification of patterns of the ways in which students were
using the learning network architecture, including its constituent parts (assemblages).
The data analysis further led to identification of the ways in which the potential for
learning they offered (affordances) were perceived, taken up and used by participants
in their networked learning practices. As a result, the analysis revealed three types of
socio-environmentally influenced relationships between the types of learning
networks, the constituting elements of learning networks, and the respondents (see
section 5.1 for more details). Furthermore, the analysis revealed the type of activities
research participants engaged in while using learning networks (networked learning
practices) (section 5.2), and, finally, it led to the identification of the interference of
perceptions (section 5.3) between student and academic teaching staff participants. All
these phenomena appeared to impact on students’ agency within their identified
networks.
3.3.1 Quantitative data analysis
In phase one, the process of quantitative data analysis, as described by Creswell
(2014), was applied. That is, first, the data were prepared and organised for analysis
(Creswell, 2014) by collecting the paper-based questionnaires from both the student
and academic teaching staff cohorts, and manually transferring the responses into
Microsoft Excel spreadsheets. Next, histograms of responses to each question were
formed to enable preliminary identification of patterns in the response data.
122 Research design
As the research questions required description of participants’ perceptions, to
confirm the preliminary identification of patterns, the data analysis used descriptive
statistics “that indicate general tendencies in the data” (Creswell, 2014, p. 202).
Statistical analysis was completed using Sigma Plot version 14, a software
package for scientific graphing and statistical data analysis. That is, the uptake of
learning affordances was determined by calculating medians of students’ responses
regarding the relevant measure values, that is importance, intensity, frequency and
sequence (when applicable) and the uptake for each affordance. The interquartile
ranges (IQR) (25-75%), or the measures of variability of the calculated medians were
also included, especially to determine the final ranking of students’ responses (from
highest to lowest). That is, when the median values were the same, the IQR was
considered as an indicator of spread of the median values. When the unit with higher
values of IQR had the same median, the unit with an IQR including higher response
values was considered to be ranked higher than the other unit. For example, if the IQR
for unit E was 5 to 7 and the IQR for unit S was 5-8, Unit S would be ranked higher,
because the range includes an 8. Furthermore, as the data were collected from two
units, the Mann-Whitney test was carried out to investigate whether the two cohorts
should be treated as one, or analysed separately. This was an important aspect of the
data analysis as it provided evidence of significant differences (i.e., more than 5%
level) between the two populations in numerous affordances across both units. Thus,
it was decided to investigate the units separately. When the test results did not indicate
differences, the units were discussed jointly.
As a result of statistical data analysis, the tables presenting the data in ranks,
following the descending order of medians, were made. These tables were used in
Chapter 4 (Findings) to present and interpret the data.
3.3.2 Qualitative data analysis
According to Hatch (2002), “data analysis is a systematic search for meaning” (p. 148);
however, this search posed many challenges, especially in relation to qualitative
research where the “discussion of analysis clearly is not as common as is the case for
quantitative research” (Leech & Onwuegbuzie, 2007, p. 562). In an overview of
qualitative data analysis methods, Leech and Onwuegbuzie (2007) discussed many
positive aspects of qualitative data, one of which was that qualitative data “can be used
to strengthen quantitative research designs in general” (p. 560). However, to achieve
Research design 123
such a positive outcome, data analysis methods need to be carefully selected,
depending on the objectives of the research, the data collection techniques, and the
types of qualitative data available (e.g., interview data, questionnaire data,
observational data, journals, notes, or transcription of focus group activities).
Literature (Hatch, 2002; Leech & Onwuegbuzie, 2007) describes numerous methods
of analysing qualitative data, such as constant comparison analysis (Glaser & Strauss,
1967; Miles & Huberman, 1994; Ryan & Bernard, 2000), keywords-in-context
(Fielding & Lee, 1998), word count (Pennebaker, Mehl, & Niederhoffer, 2003),
classical content analysis (Onwuegbuzie & Teddlie, 2003), or six-steps qualitative data
analysis (Creswell, 2014), to name just a few.
Considering that the study used mixed research methods to collect quantitative
and qualitative data, the decision was taken to apply a content analysis method (Elo &
Kyngäs, 2008; Leech & Onwuegbuzie, 2007; Onwuegbuzie & Teddlie, 2003), to
enable systematic content analysis of written data from two sources: open-ended
questions and/or comments included in the questionnaires, and focus group transcripts.
Content analysis is a well-established method, effective especially for
analysing human communication, including written, verbal and/or visual (Cole, 1988).
Elo and Kyngäs (2008) explain that “content analysis is a research method for making
replicable and valid inferences from data to their context, with the purpose of providing
knowledge, new insights, a representation of facts and a practical guide to action” (p.
108).
One of the advantages of content analysis is its versatility allowing the
approach to be used in an inductive or deductive manner, depending on the need of the
research and the nature of data collected (Elo & Kyngäs, 2008). While the inductive
approach is recommended to analyse sparse data, with no evident leading theme/
pattern/ trend and presenting “fragmented knowledge” (Elo & Kyngäs, 2008, p. 109),
the deductive approach is used when the leading theme, pattern or trend is evident and
forms a hypothesis or a theory (Elo & Kyngäs, 2008).
3.3.2.1 Questionnaire
As the open-ended questions were included in the questionnaire to explore the
“fragmented information”, that is, the sparse data scattered across open-ended
questions and comments, for parts of the quantitative data, an inductive approach to
content analysis was used.
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The inductive data analysis procedures, described by Elo and Kyngäs (2008),
are very similar to Creswell’s (2014) six-step qualitative data analysis and include the
following steps:
1. Preparing the data for analysis. The data were transferred from paper-based
questionnaires to a Microsoft Excel spreadsheet and checked for accuracy.
2. Selecting the unit of analysis. After the initial reading of the data, it was
decided that one unit of analysis will consist of an answer of each, individual
respondent, regardless the length of the answer.
3. Coding of the data, which consisted of searching for recurring concepts.
Open coding involved using different colours (diverse means of
representing information) to code different, identified concepts.
4. The identified concepts were organised in overarching categories and sub-
categories.
5. The identified categories and sub-categories were then reported and their
content described and interpreted in Chapter 4 (Findings).
6. Finally, the results of the inductive data analysis were used to formulate
preliminary hypotheses, addressed in focus groups which were analysed
using deductive data analysis methods.
By applying an inductive approach, qualitative analysis complemented the quantitative
data and provided necessary insights, thereby enabling more refined findings to be
formulated. Phase two, described in the next section, focused on deductive analysis of
focus group data.
3.3.2.2 Focus groups
The deductive data analysis used the same procedure as described above for the
inductive data analysis. That is, the data collected through focus groups, using semi-
structured interviews were first transcribed by the researcher who carefully monitored
the fidelity of the transcription with the recording. As recommended by Silverman
(2015), apart from transcribing the verbal information, non-verbal elements of the
exchange, such as laughter, were also included in the transcript, as they constitute an
important feature of the overall situational context within which the activity was
completed. The transcriptions were verified for fidelity by the researcher who took this
Research design 125
opportunity to take first notes about the findings and initiate their primary
interpretation. The transcribed data were then analysed using a deductive content
analysis method (Elo & Kyngäs, 2008), which, as recommended in the literature, is
more suitable for complex qualitative data such as focus group transcriptions.
The deductive approach was composed of the following steps: 1) preparation
of the data, which can be described in terms of “making sense of the data as a whole”
(Elo & Kyngäs, 2008, p. 110), 2) developing structured analysis matrices, 3)
identifying overarching categories and sub-categories, 4) data coding according to the
categories, 5) completing correspondence comparison to earlier studies which, in the
case of this study, consisted of comparing the findings resulting from quantitative and
qualitative data analyses, and 6) reporting on the results by describing them in Chapter
4 (Findings). Such a two-stage data analysis allowed identification of learning
networks, networked learning practices, and participants’ characteristics.
3.4 TRUSTWORTHINESS OF THE STUDY
To assess the credibility of both quantitative and qualitative content analysis, similar
evaluation criteria can be used (Elo et al., 2014; Ryan-Nicholls & Will, 2009; Schreier,
2012). Lincoln and Guba (1985) proposed the term “trustworthiness” to describe a set
of such evaluation criteria applied to assess the credibility of content analysis.
Trustworthiness indicates that “the findings of an inquiry are worth paying attention
to, worth taking account of” (Lincloln & Guba, 1985, p. 290), and denotes reliability,
validity, and objectivity of the research. Reliability can be described as a process of
“examining stability” (Creswell, 2014, p. 201), while validity is a process of examining
the accuracy of the findings by using validity strategies such as data triangulation,
member check-in, or by using rich descriptions (Creswell, 2014, pp. 201-202). Finally,
objectivity means that the same phenomenon was observed by many observers and the
final and agreed opinion was based on “their collective judgement” about the
phenomenon (Lincoln & Guba, 1985, p. 292).
To preserve the scientific nature of the research and assure its reliability,
validity, and objectivity, the following set of guidelines (McKenney, Nieveen & van
den Akker, 2006; Plomp, 2013) was observed:
1. Having an explicit conceptual framework;
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2. Developing a congruent study design, i.e. applying a strong chain of
reasoning with each phase of the study having its own design;
3. Using triangulation (of data source, data type, method, etc.) to enhance the
reliability and internal validity of the findings;
4. Using relevant analytical framework for data analysis applying both
inductive and deductive methods;
5. Using full, rich descriptions of the context, design decisions and research
results; and
6. Ensuring member check, i.e. taking data and interpretations back to the
source to increase the internal validity of findings.
These guidelines were systematically addressed and followed throughout the study.
That is, with regards to the first guideline, an explicit and comprehensive research-
underpinned conceptual framework was developed, which has been discussed in
Chapter 2. Regarding guideline two, congruence of design of the study was assured by
mapping study phases against research questions first and next against research
functions and relevant research methods (section 3.2.2).
Guideline three, triangulation for validity and reliability of the research, was
assured by applying mixed research methods explained in section 3.1, and setting up
the sequential mixed model (quantitative first) with integrated qualitative instruments
design for data collection (section 3.1.2), and by using diverse methods of quantitative
and qualitative data analysis.
In addition, as the data analysis was based on predominantly self-reported data,
which poses potential challenges to the validity, reliability, and objectivity of the study
(for a more detailed discussion, see section 5.7 Limitations), the study followed the
recommendations derived from the literature on self-reported data (Gonyea, 2005) in
order to address this issue. That is, when applicable, the data were complemented by
available university records on student demographics (section 3.2.3). Moreover, the
possibility of dishonest responses, social desirability and halo error were
systematically addressed by assuring anonymity, using a broad range of question types
as well as numerical and non-numerical scales (for a more detailed discussion see
section 5.7 Limitations).
Research design 127
Such a variety of research instruments ensured diversification of data sources
and types. To further preserve validity and reliability of the study, the additional
triangulation of the data was assured by cross-referencing responses depending on the
targeted cohort. That is, the staff and student questionnaires, despite their apparent
similarity, in fact diverged and served different purposes. While students were asked
to self-reflect on their learning and teaching environment and learning practices, the
academics’ version of the questionnaire focused on academics’ reflections on students’
practices and students’ uptake of opportunities for learning provided by the
educational environment created within the units of study under investigation. In this
way, a cross-referencing of findings was assured. Finally, as already mentioned in
section 3.2.2, the design and development of the data collection tools was research-
informed. The primary aim of triangulation of research methods is to ensure that the
weakness of one method is counteracted by the strength of other methods (Nieveen &
Folmer, 2013). In this way, not only are reliability and validity of the scientific
investigation preserved, but it also warrants objectivity of research. It is evident within
this study that the above-outlined robust and complex triangulation warranted the
objectivity of the findings. Literature on research methods points to the fact that
appropriate triangulation is also effective in promoting researcher objectivity by
minimising their bias (Miles & Huberman, 1994). As already mentioned above (e.g.
neutrality with regards to focus group), precautions were taken to preserve the
objectivity of the research by implementing robust and systematic triangulation that
counterbalanced (at least to some extent) the potential researcher bias.
Regarding guideline four, using a relevant analytical framework, quantitative
data were first analysed using Microsoft Excel. Then, Sigma Plot version 14, a
software package for scientific graphing and statistical data analysis was used for more
in-depth, sophisticated analysis (section 3.3.1), while qualitative data were analysed
using the above-discussed content analysis methods: inductive data analysis applied
for open-ended questions/ comments included in questionnaires, and deductive data
analysis used for focus groups (Cole, 1988; Elo & Kyngäs, 2008) (section 3.3.2).
Guideline five, using full, context-rich descriptions, was addressed by
providing a well-structured description of the functional contexts of both units
(sections 1.8.1 and 1.8.2), complemented by a systematic discussion of the findings
(Chapter 5).
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Finally, guideline six (member check) required comparing the findings with
the original data source. This guideline was followed to an extent, but difficulties with
contacting students and some sessional tutors did limit this. The member check was
partially assured during focus groups, when two hypotheses ensuing from the data
analysis were tested. In addition, the findings were discussed with the supervisory
team, composed of academic researchers and practitioners who were in a position to
evaluate the validity and reliability of the findings based on their expertise and
experience in educational research.
All the above-discussed guidelines were observed with the ultimate aim of
maximising the possibility to generalise findings. The qualitative aspect of the study
makes it challenging to assure generalisability of the findings. Literature indicates that
for qualitative research it is very challenging to generalise results (Yin, 2003; 2013;
2014) as this type of research is anchored within a specific environment, or “local
conditions” (Cronbach, 1975, p. 125), which are subject to constant change. Due to the
changing nature of local conditions, “any generalisation is a working hypothesis, not
a conclusion” (Cronbach, 1975, p. 125). To overcome this difficulty, Yin (2003; 2013;
2014) suggests analytic generalisation. Reflecting on generalisability of case studies,
Yin (2013) argues that the generalisability of findings, along with ensuing principles
and relating theories, should be tested through multiple replications: “by analytic
generalization is meant the extraction of a more abstract level of ideas from a set of
the case(s) in the original case study” (p. 325).
By using mixed methods research, this study has attempted to ensure the
generalisability of its findings by identifying elements of learning networks and
networked learning practices in two distinct units, as opposed to one unit. Moreover,
the decision to analyse the two units separately was based on results of a Mann-
Whitney statistical test, which provided sufficient grounds for treating both units
separately.
Furthermore, as observed above (section 3.2.2), by using a theory-seeking case
study research methodology, the study enabled fuzzy generalisations (Bassey, 1999)
to be made. Bassey (1999) observed that a well-designed educational case study should
enable fuzzy generalisations to be made, which can then provide foundations for
developing a theoretical framework. It is important to clarify that in this context, the
term fuzzy generalisation refers to high level trustworthiness and reliability of the
Research design 129
study, providing “coherence to many research endeavours in education and dispelling
the charge that educational researchers are engaged in trivial pursuits” (Bassey, 1999,
p. 13). The generalisations are made, and their “fuzziness” is inherent to, and results
from, the overall methodological challenge of making generalisations within the social
sciences disciplines, especially when research uses only qualitative research methods
(Cronbach, 1975; Yin, 2003; 2013; 2014). Nevertheless, the findings of the study
allowed two general outcomes to be formulated. First, the conclusions (section 6.1)
and implications (section 6.2) of the study provided directions for futher redesign of
the units at both, unit and curriculum levels. Second, the findings also contributed to
the conceptualisation of the curriculum and learning design framework of connected
epistemic domains. This level of abstraction is a form of genralisation of findings
providing evidence of the generalisability of the project in its entirety.
It is impossible to assure high quality standards of a research project without
complying with ethical standards. The next section briefly summarises the ethical
considerations addressed in the current research.
3.5 ETHICS
Ethical issues were addressed before commencing the data collection by successfully
applying for Ethical Clearance to the Human Research Ethics Committee at
Queensland University of Technology (Ethical Clearance 1700000159). The
application was considered low risk and approved without changes being requested.
The ethical protocols were closely observed at all stages of the research to ensure
confidentiality and anonymity of the participants. The next sections describe data
collection protocols and procedures using different instruments (questionnaires and
focus groups) with different groups of targeted prospective participants.
3.5.1 Data collection protocols and procedures: students
Recruitment of participants was based on ethical considerations and fully complied
with ethical standards, as specified in the National Statement on Ethical Conduct in
Human Research (2015). There was no pre-existing relationship between the
researcher and the targeted participants, except for the two unit coordinators, who were
work colleagues based in the same faculty as the researcher. The lack of pre-existing
relationship posed, to some extent, a challenge. For this reason, the two academics
coordinating the targeted units (work colleagues), became the intermediaries to recruit
130 Research design
potential research participants. Literature (Bloor, Frankland, Thomas & Robson, 2001)
suggests the use of an intermediary for recruitment purposes alerts researchers to the
ethical implications of such a strategy, for example, the risk of not adequately
disclosing detailed information about the research to potential participants.
Cautious with this potential ethical problem, the researcher worked in
collaboration with the academics teaching the units to follow the approved recruitment
strategy. That is, the activity was first advertised to targeted cohorts by academics
teaching the units by sending an announcement via the official Learning Management
System site to all students. The message contained information about the activity,
details of the researcher, and the Participant Information Sheet and Consent Form. In
this way, students were provided in advance with full information and the requirement
of providing all details to prospective participants was fulfilled. The coordination of
data collection was also planned, ensuring that the activity would cause only minimal
inconvenience, by taking a small amount of students’ time, in class, to fill in a paper-
based questionnaire. Data collection took place during timetabled tutorials, either
during break time or at the end of the tutorial. Usually, the tutors would either have
already left the classroom, or continued working with students by responding to their
individual or group questions and were otherwise not involved in the data collection
process. Students were reassured about the entirely voluntary nature of their
participation. To preserve student anonymity, the questionnaires did not ask for any
information (e.g., name, student number, lecture time, tutorial group) that might
allowed the respondent to be identified. Volunteering students were provided with the
choice of either filling in the questionnaire in the classroom or completing it after the
tutorial. Most respondents completed the questionnaire in the classroom, but some
opted to take the questionnaires and return them later to the indicated collection point
(located at a Student Services office). If collected at the office, the questionnaires were
collected by the office staff who next gave them the documents in a sealed envelope
to the researcher. In this way, the anonymity of respondents was preserved.
Ethical considerations also guided the recruitment and the data collection
procedures when recruiting volunteering student participants to take part in focus
groups. Again, an intermediary, an academic staff member, was used to recruit
participants. To recruit participants, announcements were made on the official LMS
sites for the units, followed by the researcher’s visits to lectures or tutorials (depending
Research design 131
on advice received) and advertising the activity. A small incentive in the form of a
coffee voucher was offered to volunteering students.
The focus groups were held on the premises of the university, in meeting rooms
booked for the occasion, and audio recorded. Robinson (2012) points out that the focus
groups encourage interaction between both the respondents and the researcher. The
participants were first provided with the Participant Information Sheets and Consent
Forms, and then with printed copies of questions, and the sessions were conducted in
a semi-structured manner. Following Creswell (2014), the researcher facilitated the
activity and made cautious efforts to maintain neutrality, prevent bias and keep the
activity on-topic. Printed copies of semi-structured questions were used and, when
needed, the researcher also made some requests for clarifications. The researcher also
ensured that all participants had time to express their opinions prior to moving to the
next question.
The above-described protocols and procedures were used to preserve
neutrality, prevent bias, and keep the focus on the research topic. In addition, by
observing the above-described strategy, the credibility of the research (Lincoln &
Guba, 1985; Cohen, Manion & Morrison, 2007; Creswell, 2014) was increased, which
contributed to the trustworthiness of the study.
3.5.2 Data collection protocols and procedures: academic teaching teams
As noted, a pre-existing professional relationship existed between the researcher and
the two unit coordinators teaching the units. However, there was no prior relationship
with the sessional academics. To recruit tutors, academics acted as intermediaries.
First, the sessional academics were provided verbally with information about the data
collection and informed that their input was sought. This was complemented by an
email sent to the tutors with the Participant Information Sheet attached. Finally, during
the data collection activity with students, a questionnaire was distributed to the
sessional academics who returned the questionnaires either in person or via email. As
for the academics (lecturers and/or unit coordinators), they were contacted at the
beginning of the activity, to advise on the data collection schedule, suggest the most
suitable ways of collecting the data, and participate in the activity. All contacted
academics agreed to fill in the questionnaire, which was provided and returned
electronically. As the data were collected through direct contact with research
participants, to preserve anonymity of respondents from the researcher, the data were
132 Research design
de-identified and transferred from the returned questionnaires to a Microsoft Excel
spreadsheet.
Focus groups were organised with the assistance of academics who enabled
contact with tutors, advertised the activity and advised on the best ways of collecting
the data, i.e. which meeting to attend, when and where. On the day of the data
collection all required documentation (i.e., the Participant Information Sheet, Consent
Form and printed copies of the questions) was distributed, and after a few minutes of
perusal time, a final check was carried out that all volunteering participants agreed to
be audio-recorded. Then the focus group was conducted in a semi-structured manner.
As advocated by Creswell (2014), during the activity, conscious effort was made to
preserve neutrality, prevent bias and keep the activity on-topic by leaving time for
respondents to finish their thoughts before asking for clarifications. In this way the
trustworthiness of the study (Lincoln & Guba, 1985; Cohen, Manion & Morrison,
2007; Creswell, 2014) was also preserved.
At the end of the both data collection activities (i.e., questionnaire and focus
group) an email to all academic teaching staff was sent expressing gratitude for their
help, collaboration, and support in the research.
3.6 REFLECTION ON RESEARCH METHODOLOGY
Using a metaphor of a human body, research methodology could be described as ‘the
backbone, the spine’ of the research. It holds all components of the research together
and ensures they have a shape and ‘keep a straight posture’. Reflecting on the
importance of research methodology, Cohen, Manion and Morrison (2007) write:
Ontological assumptions […] give rise to epistemological
assumptions […]; these, in turn, give rise to methodological
considerations; and these, in turn, give rise to issues of
instrumentation and data collection. Indeed, added to ontology and
epistemology is axiology […]. This view moves us beyond regarding
research methods as simply a technical exercise and as concerned
with understanding the world; this is informed by how we view our
world(s), what we take understanding to be and what we see as the
purposes of understanding, and what is deemed valuable. (p. 3)
Research design 133
This chapter, by providing a strong rationale for selecting the above-described
research methodologies, data collection instruments and data analysis methods, has
been a careful introduction to the multi-layered findings (Chapter 4) and their
interpretation (Chapter 5).
134 Research design
Findings 135
Findings
This chapter systematically addresses the three research questions:
1. What learning affordances offered by presupposed learning networks were
perceived and taken up by undergraduate STEM students?
2. What networked learning practices are occurring within the presupposed
learning networks?
3. What are academic staff participants’ perceptions of students’ networked
learning practices within the presupposed learning networks?
The chapter is an in-depth analysis of self-reported claims and statements
collected from research participants through mixed methods research (section 3.2). To
ensure the presentation of findings aligns with the ontological paradigm and
epistemological underpinnings of the research, as discussed in the Literature Review
(Chapter 2), the structure of the chapter builds on the definition of a productive
learning network and productive networked learning practices (developed in section
1.5, section 2.2 and section 2.3). In response to the first research question, section 4.1
provides an in-depth analysis of the presupposed architecture of learning networks,
based on self-reported data collected from respondents. That is, this section analyses
student respondents’ claims and statements regarding their perception and uptake of
learning affordances offered by formal and informal learning networks. Next, in
response to research question two, section 4.2 offers an in-depth analysis of student
respondents’ claims and statements related to their learning practices within identified
learning networks. Finally, as the learning and teaching process implies collaboration
between both students and academic teaching staff, section 4.3 provides a response to
the last research question by presenting an in-depth analysis of academics’ claims and
statements regarding their perceptions of uptake of learning affordances by the
students they teach. Figure 4.1 presents a schematic representation of the presupposed
learning networks.
136 Findings
Figure 4.1: Presupposed architecture of learning networks.
Table 4.1 summarises the identified constituting elements of the presupposed earning networks.
Table 4.1: Summary of constituting elements of presupposed learning networks.
Constituting elements of a learning network
Constituting elements of a presupposed learning network
Learning and teaching approaches (ideas).
Timetabled activities: LecturesWorkshopsComputer laboratories
People directly or indirectly involved in the student participants’ learning.
University teaching staff (academics, casual academics teaching the unit under investigation)University support staff
Findings 137
Fellow students enrolled in this unitStudents from other universitiesTeaching staff from other universitiesProfessionalsPrivate tutorsFamily member (not professional or from the university)
Learning tools available within student participants’ learning environment.
Electronic devices: - Desktop computer- Laptop- Tablet- Mobile phone
Textbooks and eBooksOnline learning tools:
- Unit Blackboard site (including alllearning resources and documentsaccessible in digitalised format)
- QUT Library site- Specialised Internet websites (e.g.,
journal websites, other universities)- This unit’s Facebook- Respondent’s personal Facebook- Specialised blogs- Specialised chat rooms- You Tube- Khan Academy- Internet (general access to the Internet)- Mobile phone apps- Social media (e.g., Twitter)- Professional network communities (e.g.
Open Source)- MOOCs
Artifacts produced by student participants.
Learning materials produced onlineLearning notes produced electronicallyLearning notes made by hand
138 Findings
Learning practices of student participants.
Studying for timetabled activitiesAttendance/ non-attendance at the timetabled activitiesCatching up with missed content of timetabled activities
The next sections systematically discuss the above-specified content of the
chapter. The chapter concludes with a summary of findings, followed by a reflection
considering whether the presupposed learning networks and the networked learning
practices could be described as productive. The reflection leads to the discussion and
interpretation of findings presented in Chapter 5 (Discussion).
4.1 RESEARCH QUESTION 1: WHAT LEARNING AFFORDANCES OFFERED BY PRESUPPOSED LEARNING NETWORKS WERE PRECEIVED AND TAKEN UP BY UNDERGRADUATE STEM STUDENTS?
This section starts with a detailed description of the frames of reference of students
participating in the study (section 4.1.1). This description attracts attention to the
richness and diversity of student experiences, both personal and educational, which
impacted on perceptions and uptake of the learning affordances provided by the formal
learning networks of the units. Next, an in-depth inductive and deductive analysis of
each constituting element of learning networks is presented (section 4.1.2), outlining
each identified learning affordance, how they are perceived, and taken up. To present
an in-depth analysis and rich findings, each uptake is analysed in terms of the
affordance’s importance, frequency of uptake, and intensity of engagement (as
explained in section 3.2.4.1.1). When applicable, sequence of uptake is also
investigated.
In summary, the findings indicate which affordances were taken up and what the
role of frames of reference was in this process.
Findings 139
4.1.1 Students’ frames of reference
This section investigated respondents’ frames of reference from the perspective of
respondents taking on the identity of university students. For this reason, the study
used a questionnaire and focus group to explore respondents’ motivation to enrol in
their degree of choice and the development of their identity as university students. That
is, the study investigated whether (and how) respondents were developing their sense
of belonging to the university community, their preferred ways of socialising (on-
campus vs online), and their involvement in extra-curricular activities.
An in-depth analysis of students’ focus revealed an important diversity
amongst respondents’ motivations to undertake their degree. The focus group
respondents represented a broad spectrum of students, ranging from mature-age
students to younger students still in high school, and could clearly explain the reasons
for undertaking their university degree. Based on their comments, six overarching
concepts have been identified: 1) interest in the discipline; 2) interest in applying
engineering/ science concepts in the real life settings; 3) pleasure of learning; 4)
willingness to contribute to society; and 5) to get a job. Interestingly, of all six groups,
only one group briefly mentioned getting a job as their motivation for enrolling in a
degree; the remaining groups provided more complex reasons. For instance, mature
aged students pointed towards the importance of having a university degree for further
career steps, being promoted, or finding more interesting work. One student stated:
I just need a piece of paper to take me to the next level. But also it
was something I wanted to do just to complete my studies, and
that’s my motivation behind being here. (Respondent 1, Focus
Group 3, lines 7-9)
Younger respondents demonstrated less practical-oriented motivation,
focused on learning, developing oneself, and contributing to society. One
respondent stated:
I decided quite early off that I wanted to help people, I am a giver, I
like seeing development and seeing changes in people’s life. And
obviously one of the biggest factors in someone’s life is health. So,
140 Findings
I thought Medical Engineering would be a great tool to improve
someone’s life. […] I saw it from a larger picture and I thought that
if I can make a change or a difference in a piece of technology, and
that could be implanted in thousands of people, then that would make
a bigger effect upon society. (Respondent 1, Focus Group 6, lines
49-65)
The next element of respondents’ frames of reference, their sense of becoming
university students, was investigated through focus groups and a questionnaire. The
response rate to this part of the questionnaire was high across both units and ranged
between 100% and 83%, depending on the question and unit. The data analysis
revealed that, overall, the majority of respondents, 85% (64 of 75) of respondents in
Unit E and 88% (44 of 50) of respondents in Unit S, perceived themselves as “being a
university student”.
An in-depth analysis of the student focus groups provided more insight into
respondents’ understandings of the concept of “being a university student”. They felt
that being a university student involves both cognitive and social engagement.
Cognitive engagement was understood as purposeful learning that enables them to:
[A]bsorb as much information daily as you can because the whole
reason of being a student is to become a professional in something.
So that you transition from being a student to professional or to a
researcher. (Respondent 1, Focus Group 6, lines 4-8)
Social engagement was understood as taking up the affordances of learning from/ with
others. One respondent remarked:
And [when] being a student, it’s nice to be with the people and that
will accelerate your learning. [ … ] Each individual person may be
able to push themselves to learn further, but being a student, you
have so many people supporting you to push you and learn as much
as you can. (Respondent 2, Focus group 6, lines 31-38).
Findings 141
Interestingly, the above-quoted respondent did not specify whether the “people
supporting you” were experts (e.g., academics, university learning support staff) or
peers.
The importance of the social aspects of being a university student was
confirmed by further questionnaire data analysis. The results revealed that, in both
units, the majority of respondents (73% in Unit E and 67% in Unit S) socialised online
with other students from their university. With regards to socialising on-campus, the
percentage of students who responded positively to this question reached 76% in Unit
E and 77% of respondents in Unit S. As for respondents’ involvement in extra-
curricular activities (e.g., student clubs), data analysis revealed low levels of
engagement. In Unit E, only 35% (24 of 69) of students and 24% (14 of 58) in Unit S
were involved in this type of activities.
In summary, it appears that the respondents were in the process of building
their identity as university students based on two conceptual pillars: cognitive (i.e.,
learning) and social (i.e., being supported by others in their learning process). While
the first pillar was clearly explained in respondents’ comments, the role of the second
was less clear. That is, data analysis indicated that respondents did socialise with other
students, online and on-campus; however, the data also suggests that socialising could
have a pragmatic aspect. A majority of respondents were not involved in extra-
curricular activities that require a more proactive attitude towards building an
academic community. This raises the question of whether socialising with peers was
conducive to social learning. What were the respondents’ perceptions and uptake of
affordances for social learning? Which learning affordances were perceived as more
important and more frequently taken up: learning from or learning with experts, or
learning from or learning with peers? Looking from the perspective of this study, these
questions enquire about respondents’ choices when perceiving and taking up
affordances offered by different elements of their learning networks. The next section
presents findings of data analysis of questions about respondents’ use of their learning
networks leading to description of the architecture of their learning networks.
142 Findings
4.1.2 Elements of learning networks
The first research question investigated which, and with what intensity, elements of
learning networks facilitated interaction(s) between respondents and learning
network(s).
This was achieved by investigating learning affordances offered by the
elements of learning networks, the perception of these affordances (influenced by
frames of reference), and their uptake, triggered by the functional context and enacted
by the respondents. Following the definition of learning network introduced in section
1.5 and developed in section 2.2, the five elements of learning networks of specific
units of study under investigation were: 1) the learning and teaching approaches
(ideas); 2) the people directly or indirectly involved in the student participants’
learning environment; 3) the learning tools (predominantly digital); and 4) the artifacts
produced by participants. The fifth element, practices, is discussed under research
question two.
The next four sections systematically discuss each of the above-mentioned
elements of the learning networks with a focus on perception and uptake of learning
affordances. That is, for each element of a learning network, a typology of learning
affordances and opportunities for interactions they offer was developed, based on
previous experience and in consultation with the supervisory team, academics, other
researchers and students. The typology was used in the questionnaire to investigate the
perceived importance, intensity, sequence and frequency8 of uptake of identified
learning affordances.
The first element of the learning network, ideas, sheds light on the relationship
between the student respondents’ interactions with the content, discipline knowledge,
and the academic teaching staff involved in the unit, in the context of formal and
informal learning networks. The second element, people, required social interaction
between social agents, constituents of learning networks. The investigation
distinguished between two types of social agents: experts and peers, and two types of
learning networks: formal or informal. Tools, the third element forming learning
networks, offered different types of learning affordances and opportunities for
interaction. Hence, the typology of learning affordances offered by the tools reflected
8 Sequence and frequency were investigated when relevant.
Findings 143
respondents’ levels of autonomy when consciously selecting, or searching for, an
online tool that could assist them with their learning. The typology of learning
affordances offered by artifacts also reflected the criterion of autonomy, especially
when processing and/ or developing respondents’ own learning resources. Creating a
learning resource is evidence of learners’ high levels of autonomy, as it requires de/-,
re- and co-construction of knowledge. On the other hand, downloading learning
resources without manipulating them in any way indicates a low level of learning
autonomy. The next four sections discuss the details.
4.1.2.1 Ideas
In the context of this study, the concept of ideas refers to learning and teaching
approaches used during timetabled activities, which are elements of formal learning
networks applied in the two units under investigation for face-to-face, on-campus
learning (see section 1.7 for details). These were lectorials and workshops in Unit E,
and lectorials, workshops, and computer laboratories in Unit S. The study enquired to
what extent the learning affordances offered by ideas were perceived by research
participants, how important they were and how intensely they were taken up. The study
also investigated what type of intended interaction seemed to be enabled by timetabled
activities.
4.1.2.1.1 Typology of learning affordances and corresponding intended
interactions
The overall aim of the questions was to investigate to what extent learners were active
in searching for opportunities to interact with and within learning networks to take up
the opportunities for learning. This is an important question to investigate, as it informs
whether the networks were productive. That is whether they “enabled shared learning
resulting from collaborative, coordinated and purposeful activity of co-creating
knowledge” (Carvalho & Goodyear, 2014a), and whether they facilitated feeding back
the knowledge to the network (Downes, 2010a, 2012; Siemens, 2004, 2005a, 2005b;
2006; 2019; n.d.). This aim was achieved by investigating whether the respondents
preferred to learn from experts/peers by listening to the lectures, which would suggest
a preference towards knowledge assimilation. Another affordance was to learn with
experts/peers by, for example, asking questions for clarification, which would suggest
respondents’ engagement in co-construction of knowledge and “knowledge creation”
144 Findings
(Kop & Hill, 2008, p. 2) through collaborative, coordinated and purposeful activity.
The distinction between experts and peers was made based on Bronfenbrenner’s EST,
with experts being understood as specialists in domain knowledge and belonging to an
individual’s mesosystem. With regards to peers, these are described as either friends
(mates), thus belonging to an individual’s microsystem, or more broadly classmates,
part of an individual’s mesosytem.
The questions also investigated the respondents’ reasons for attending the
timetabled activities, for example learning, socialising, or learning about assessment
only. Finally, the questionnaire enquired whether respondents were meeting members
of their academic community such as academic teachers, classmates and peers, and
whether they were developing self-regulation, an important aspect of being an
autonomous university student.
A typology of learning affordances with associated intended interactions, as
identified across all three timetabled activities, was developed and is presented in
Table 4.2.
Table 4.2: Identified affordances in all timetabled activities and the opportunities for action they offer.
Timetabled activity
Learning affordance Intended interaction
Lectures Provide new content Learning the contentMeet face-to-face with the lecturer Social interaction with
expertsMeet face-to-face with other students Social interaction with
peersLearn directly from the lecturer Learning from expertsLearn directly from other students Learning from peersAsk questions/ seek clarifications directly from the lecturer
Learning with experts
Ask questions/ seek clarifications directly from other students
Learning with peers
Learn about assessment in this unit Learning for assessmentWorkshops Practise the new content presented at the lecture Learning/ practising the
content Work collaboratively with other students Learning with peersWork collaboratively with help from teacher, if needed
Learning with experts
Findings 145
Meet face-to-face with the teachers Social interaction with experts
Meet face-to-face with other students Social interaction with peers
Practise for assessment Learning for assessmentPractise learning skills (work organisation, time-management, focus, etc.)
Self-regulation
Computer laboratories
Practise the new content presented at the lecture and the workshop
Learning/ practising the content
Work individually, with help from the teachers, if needed
Learning from experts
Work individually with help from other students, if needed
Learning from peers
Meet face-to-face with the teachers Social interaction with experts
Meet face-to-face with other students Social interaction with peers
Access specialised software Practical reasonsPractise for assessment Learning for assessmentPractise learning skills (work organisation, time-management, focus, etc.)
Self-regulation
Each timetabled activity was mapped against the different type of interaction it
enabled, such as: learning (focus on cognition), learning with/ from experts, learning
with/from peers, social interaction with experts/ peers, or self-regulation (becoming a
conscious and reflective learner).
The next section presents the results of the questionnaire followed by a short
summary of findings.
4.1.2.1.2 Uptake of learning affordances and types of enacted interactions
The first question in this section investigated attendance at all three timetabled
activities. The data were collected during face-to-face, timetabled activities (e.g.,
lectures, workshops), and thus reflect opinions of student respondents who had already
made the decision to attend in person, at least on the day of data collection. The
response rate was high with 100% from both units responding to this question. Figure
4.2 summarises the data from both units.
146 Findings
Figure 4.2: Student attendance at timetabled activities in Unit E (n=75) and in Unit S (n=60).
Data analysis revealed a pattern of attendance in both units and across all three
timetabled activities. That is, those respondents who committed to attending the
timetabled activities were consistent in their practice, with very few not attending or
attending only occasionally.
Next, respondents were asked to rank the importance and the intensity of
uptake of each suggested learning affordance for all three timetabled activities.
Overall, the response rate was high for all types of timetabled activities, ranging on
average from 95% to 50% across both units. Finally, the questionnaire enquired
specifically about two variables – importance and intensity, as this differentiation
offered a potential to collect more nuanced information about the relationship between
perceiving an opportunity for an action, considering it important and actually taking it
up with a certain intensity
The next three tables, Table 4.3, Table 4.4 and Table 4.5, summarise the results
of the data analysis for lectures, workshops and computer laboratories. While lectures
and workshops were offered to respondents from both units, the computer laboratories
were available to respondents in Unit S only.
32
40
51
42
23
35
19
24
10
14
4
1
4
11
3
3
7
1
1
5
0 10 20 30 40 50 60
Lectures unit E
Lectures unit S
Workshops unit E
Workshops unit S
Computer Labs unit S
NUMBER OF RESPONDENTS
TIM
ETAB
LED
ACTI
VITY
Never Occasionally Sometimes Most of the time Always
Find
ings
147
Tabl
e 4.
3:Su
mm
ary
of id
entif
ied
med
ian
valu
es, i
nter
quar
tile
rang
es a
nd p
valu
es o
f im
porta
nce
and
inte
nsity
of u
se o
f the
lect
ures
in b
oth
units
.
TIM
ET
AB
LED
AC
TIV
ITY
(L
ectu
res)
IMPO
RTA
NC
EIN
TE
NSI
TY
Aff
orda
nces
Gro
upn
Med
ian
(8-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5% le
vel
nM
edia
nIn
terq
uart
ile
rang
e25
-75%
P*s
igni
fican
t at
5% le
vel
Prov
ide
new
con
tent
U
nit E
758
7-8
0.10
275
87-
80.
783
Uni
t S60
76-
860
87-
8M
eet f
ace-
to-f
ace
with
the
lect
urer
U
nit E
745
2-5
* 0.
011
734
2-5
* 0.
041
Uni
t S60
4.5
3-6
605
3-6.
5Le
arn
dire
ctly
from
the
lect
urer
Uni
t E74
75-
80.
625
746
4-7
0.61
1U
nit S
607
5-7
606
5-7
Mee
t fac
e-to
-fac
e w
ith o
ther
stud
ents
U
nit E
743.
53-
50.
326
734
2.25
-60.
872
Uni
t S60
32-
560
53-
6Le
arn
dire
ctly
from
oth
er st
uden
ts U
nit E
743
2-5
0.57
473
42-
50.
176
Uni
t S60
32-
460
32-
5A
sk q
uest
ions
/ see
k cl
arifi
catio
ns
dire
ctly
from
the
lect
urer
Uni
t E74
43-
6*
0.02
773
42.
25-6
0.87
2U
nit S
605
3-6.
560
53-
6A
sk q
uest
ions
/ see
k cl
arifi
catio
ns
dire
ctly
from
oth
er st
uden
tsU
nit E
744
3-6
* 0.
032
735
3-6
* 0.
017
Uni
t S60
32-
560
32-
5Le
arn
abou
t ass
essm
ent i
n th
is u
nit
Uni
t E75
6.5
5-6
* 0.
042
756
5-7
0.15
3U
nit S
605
2-6
605.
53-
7
148
Find
ings
Tabl
e 4.
4:Su
mm
ary
of id
entif
ied
med
ian
valu
es, i
nter
quar
tile
rang
es a
nd p
valu
es o
f im
porta
nce
and
inte
nsity
of u
se o
f the
wor
ksho
ps in
bot
h un
its.
TIM
ET
AB
LED
AC
TIV
ITY
IMPO
RTA
NC
E
(Wor
ksho
ps)
INT
EN
SIT
Y
Aff
orda
nces
Gro
upn
Med
ian
(7-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5%
leve
ln
Med
ian
(7-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5%
leve
l
Prac
tise
the
new
con
tent
pr
esen
ted
at th
e le
ctur
eU
nit E
757
6-7
0.10
374
77-
7*
0.03
0U
nit S
607
5.75
-760
76-
7W
ork
colla
bora
tivel
y w
ith o
ther
st
uden
tsU
nit E
754.
53-
60.
550
745
5-6
0.79
5U
nit S
605
3.75
-660
53-
6W
ork
colla
bora
tivel
y w
ith h
elp
from
teac
hers
, if n
eede
dU
nit E
755
4-6
0.36
374
44-
60.
073
Uni
t S60
55-
660
54-
6M
eet f
ace-
to-f
ace
with
the
teac
hers
Uni
t E75
31.
5-5
0.67
174
32-
4.25
0.58
4U
nit S
603
2-5
603
3-4.
5M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
Uni
t E75
32-
40.
796
743
1-4
0.28
1U
nit S
603
4-2
603
1-4
Prac
tise
for a
sses
smen
tU
nit E
755.
54-
60.
458
744.
53.
25-6
0.41
1U
nit S
604
2-6
605
3-6
Prac
tise
lear
ning
skill
s (w
ork
orga
nisa
tion,
tim
e-m
anag
emen
t, fo
cus,
etc.
)
Uni
t E75
11-
3.75
0.75
075
1.5
1-5
0.38
6U
nit S
601
1-3.
7560
11-
4
Find
ings
149
Tabl
e 4.
5:Su
mm
ary
of id
entif
ied
med
ian
valu
es, i
nter
quar
tile
rang
es a
nd p
valu
es o
f im
porta
nce
and
inte
nsity
of u
se o
f the
com
pute
r lab
s in
Uni
t S
only
.
TIM
ET
AB
LED
AC
TIV
ITY
(Com
pute
r L
ab)
IMPO
RTA
NC
EIN
TE
NSI
TY
Aff
orda
nces
nM
edia
n (8
-1)
Inte
rqua
rtile
ra
nge
25-7
5%n
Med
ian
(8-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
Prac
tise
the
new
con
tent
pr
esen
ted
at th
e le
ctur
e (a
nd th
e w
orks
hop)
498
6-8
428
6-8
Wor
k in
divi
dual
ly, w
ith h
elp
from
the
teac
hers
, if n
eede
d49
75-
741
75-
7
Prac
tise
for a
sses
smen
t49
63-
742
64-
7W
ork
indi
vidu
ally
with
hel
p fr
om o
ther
stud
ents,
if n
eede
d40
53-
65
3-6.
25
Mee
t fac
e-to
-fac
e w
ith th
e te
ache
rs39
43-
533
53-
6
Acc
ess s
peci
alis
ed so
ftwar
e41
43-
635
43-
5M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
353
2-4
303
2-4.
25
Prac
tise
lear
ning
skill
s (w
ork
orga
nisa
tion,
tim
e-m
anag
emen
t, fo
cus,
etc.
)
362
1-5
321.
51-
4.75
150 Findings
The statistical analysis revealed six affordances (four for importance and two for
intensity), with p values significant at a level of 5% in relation to the question about
lectures, and one significant difference in the intensity of the uptake of affordances in
workshops. This result shows the statistical differences between the two student
populations and presents an argument in favour of analysing the two populations
separately. This is an important finding, especially considering the fact that the
statistical data analysis also revealed consistently similar median values of the same
affordance across both units. The difference in median values, when identified, was
small and ranged between 0.5 and 1 point. For instance, as shown in Table 4.2, when
analysing the importance of a particular affordance, the difference between the median
values of the first affordance (“Provide new content”), and across the two units was 1
point, while the difference between median values of the second affordance (“Meet
face-to-face with the lecturer”) was 0.5 point. This finding suggests that the
perceptions of the student populations in both units were similar. Furthermore, the data
analysis also revealed consistent intra-affordance similarity between the importance
and intensity of uptake across all types of timetabled activities, and across both units.
All differences were minor, ranging between 0.5 and 1 point, with only one exception
(Table 4.2, affordance “Meet face-to-face with other students” with the difference
between the median values reaching 2 points). This result indicates that the affordances
that were perceived as important, were also self-reported to be taken up intensely,
while those affordances that were not considered important, were not taken up
intensely.
As the importance seemed to be the dominant indicator of uptake of
affordances, a ranking of perceived affordances was established based on the median
values of perceived importance. The ranking also incorporated a mapping of the
affordances against the above-mentioned intended interaction to investigate which
intended type of interaction was successfully facilitated by learning networks and in
what order. The next series of three tables (Table 4.6, Table 4.7 and Table 4.8)
summarise the results.
Find
ings
151
Tabl
e 4.
6:Ty
pes o
f lea
rnin
g af
ford
ance
s off
ered
by
lect
ures
map
ped
agai
nst t
ype
of a
ffor
danc
e an
d m
ode
of in
tend
ed in
tera
ctio
n, b
oth
unit
TIM
ET
AB
LED
AC
TIV
ITY
(Lec
ture
s)U
nit E
Uni
t S
Ran
king
Aff
orda
nces
nM
edia
n(8
-1)
Inte
rqua
rtile
ra
nge
25-7
5%R
anki
ngA
ffor
danc
esn
Med
ian
(8-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
1Pr
ovid
e ne
w c
onte
nt75
87-
81
Prov
ide
new
con
tent
607.
56-
8
2Le
arn
dire
ctly
from
the
lect
urer
747
5-8
2Le
arn
dire
ctly
from
the
lect
urer
607
5-7
3Le
arn
abou
t ass
essm
ent i
n th
is
unit
756.
55-
63
Lear
n ab
out a
sses
smen
t in
this
uni
t60
53-
6.5
4M
eet f
ace-
to-f
ace
with
the
lect
urer
745
2-5
4A
sk q
uest
ions
/ see
k cl
arifi
catio
ns d
irect
ly fr
omth
e le
ctur
er60
52-
5
5A
sk q
uest
ions
/ see
k cl
arifi
catio
ns d
irect
ly fr
omth
e le
ctur
er74
43-
65
Mee
t fac
e-to
-fac
ew
ithth
e le
ctur
er60
4.5
3-6
5A
sk q
uest
ions
/ see
k cl
arifi
catio
ns d
irect
ly fr
omot
her s
tude
nts
744
3-6
6M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
603.
52-
5
6M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
743.
53-
57
Ask
que
stio
ns/ s
eek
clar
ifica
tions
dire
ctly
from
othe
r stu
dent
s60
32-
5
7Le
arn
dire
ctly
from
othe
r st
uden
ts74
32-
58
Lear
n di
rect
ly fr
omot
her
stud
ents
603
2-4
152
Find
ings
Tabl
e 4.
7:Ty
pes o
f lea
rnin
g af
ford
ance
s off
ered
by
wor
ksho
ps (b
oth
units
) and
com
pute
r lab
orat
orie
s (U
nit S
onl
y), m
appe
d ag
ains
t typ
e of
af
ford
ance
and
inte
nded
inte
ract
ion.
TIM
ET
AB
LED
AC
TIV
ITY
(Wor
ksho
ps)
Uni
t EU
nit S
Ran
king
Aff
orda
nces
nM
edia
n(7
-1)
Inte
rqua
rtile
ra
nge
25-7
5%R
anki
ngA
ffor
danc
esn
Med
ian
(7-1
)In
terq
uart
ile
rang
e25
-75%
1Pr
actis
e th
e ne
w c
onte
nt
pres
ente
d at
the
lect
ure
757
6-7
1Pr
actis
e th
e ne
w c
onte
nt
pres
ente
d at
the
lect
ure
607
5.75
-7
2Pr
actis
e fo
r ass
essm
ent
755.
54-
62
Wor
k co
llabo
rativ
ely
with
he
lp fr
om te
ache
rs, i
f nee
ded
605
5-6
3W
ork
colla
bora
tivel
yw
ithhe
lp
from
teac
hers
, if n
eede
d75
54-
63
Wor
k co
llabo
rativ
ely
with
ot
her s
tude
nts
605
4-6
4W
ork
colla
bora
tivel
y w
ith
othe
r stu
dent
s75
4.5
3-6
4Pr
actis
e fo
r ass
essm
ent
604
2-6
5M
eet f
ace-
to-f
ace
with
othe
r st
uden
ts75
32-
45
Mee
t fac
e-to
-fac
e w
ith th
e te
ache
rs60
32-
5
6M
eet f
ace-
to-f
ace
with
the
teac
hers
753
1.5-
36
Mee
t fac
e-to
-fac
e w
ith o
ther
st
uden
ts60
31-
4
7Pr
actic
e le
arni
ng sk
ills (
wor
kor
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
751
1-3.
757
Prac
tice
lear
ning
skill
s(w
ork
orga
nisa
tion,
tim
e-m
anag
emen
t, fo
cus,
etc.
)60
11-
3.75
Find
ings
153
Tabl
e 4.
8:Ty
pes o
f lea
rnin
g af
ford
ance
s off
ered
by
com
pute
r lab
orat
orie
s in
Uni
t S (o
nly)
, map
ped
agai
nst t
ype
of a
ffor
danc
e an
d in
tend
ed
inte
ract
ion.
TIM
ET
AB
LED
AC
TIV
ITY
(Com
pute
r La
b)
Ran
king
Aff
orda
nces
nM
edia
n (8
-1)
Inte
rqua
rtile
ra
nge
25-7
5%1
Prac
tise
the
new
con
tent
pre
sent
ed a
t the
lect
ure
(and
the
wor
ksho
p)49
86-
8
2W
ork
indi
vidu
ally
, with
hel
p fr
om th
e te
ache
rs, i
f nee
ded
497
5-7
3Pr
actic
e fo
r ass
essm
ent
496
3-7
4W
ork
indi
vidu
ally
with
hel
p fr
om o
ther
stud
ents
, if n
eede
d40
53-
65
Mee
t fac
e-to
-fac
e w
ithth
e te
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rs39
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ess s
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alis
ed so
ftwar
e41
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57
Mee
t fac
e-to
-fac
e w
ith o
ther
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ents
353
2-4
8Pr
actis
e le
arni
ng sk
ills (
wor
k or
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
362
1-5
Lege
nd(f
or a
ll th
e ab
ove)
: A
ffor
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olou
rIn
tend
ed
inte
ract
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Col
our
Lear
ning
Y
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wLe
arni
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om
Blu
e Le
arni
ng w
ith
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cA
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pert
Pink
A p
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Ora
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Soci
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ing
Gre
enPr
actis
ing
Turq
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spec
ialis
ed so
ftwar
e G
rey
154 Findings
The analysis revealed that in all three timetabled activities the most important
affordance was the opportunity to learn and to practise the content. Respondents also
ranked the intensity of this affordance at the highest level, across all three timetabled
activities, and in both units.
Furthermore, the opportunity for learning from and with experts (with only one
exception of workshops in Unit E) ranked second highest for importance and intensity
of uptake in both units. Although the type of intended interaction differed depending
on the functional contexts of the units within which learning took place (i.e. learning
from during lectures vs learning with during workshops and computer laboratories),
the importance of collaborative learning with/social learning from experts was
nevertheless highly valued by all respondents, across all types of timetabled activities,
in both units. In short, it appears that the timetabled activities promoted social learning
with/ from experts in the first instance.
As for learning with/ from peers, this affordance seemed to be consistently
ranked lower than learning with/ from experts in both units and across all affordances.
The data suggests that a pattern can be constructed based on the data analysis of
responses. Independent of the type of timetabled activity, and thus the units’ functional
context, respondents indicated learning from/ with experts as more important and
intensely taken up, than learning with/ from peers. In short, it appears that respondents
perceived learning with/ from experts as slightly more important and intensely taken
up than learning with/ from peers.
Learning/ practising for assessment was the second/ third most important (and
intensely taken up) learning affordance, as reported by respondents. This finding
indicates that the respondents were attending the timetabled activities with purpose,
and took up the affordance intensely, to prepare themselves for assessment. In all
timetabled activities this affordance was highly ranked (second or third place), with
one exception of a workshop in Unit S for which practising for assessment was ranked
in fourth position.
Consistently low median perceived importance of affordances for social
interactions with both experts and peers, across all timetabled activities in both units,
seems to confirm the above observation. This affordance was ranked in sixth position,
with one exception of lectures in Unit E ranking in fourth position. This confirms the
previous finding that the respondents were primarily interested in learning from/ with
Findings 155
the experts and the respondents did not recognise or appreciate the opportunity
to connect (to meet and become community members) through interactions with
experts, their classmates and peers.
Finally, and similarly in both units and across all timetabled activities, the
opportunity to develop one’s self-regulation was consistently perceived as the least
important affordance and therefore least intensely taken up. This suggests respondents’
pragmatic attitude towards learning, as an activity of predominantly knowledge
assimilation, as opposed to “knowledge creation” (Kop & Hill, 2008, p. 2).
An analysis of open-ended comments provided further insights into the
reasons for learning from/ with experts during timetabled activities. For instance, when
commenting on the attendance at workshops, one respondent wrote: “They [tutors] are
incredibly helpful, tutors are friendly and engaging, and it [i.e. workshop] summarises
weekly content”. This personal aspect of face-to-face contact with experts during
timetabled activities became salient through the analysis of focus groups. Some
respondents reflected in the following way on the personal aspect of face-to-face
contact with experts:
Yeah, I was there today. Two hours as usual, quiet. I actually enjoy
going like one-on-one for instance. It’s so good. Because you get
talk to people. I think it depends on the personality of the tutor, some
are more approachable and some not really, which is still fine with
me. Because we are all different anyway, so… We tend to learn
when there is a bit more possibility for that [i.e., interacting], I
suppose (Respondent 1, Focus Group 2).
This comment illustrates how this particular respondent was looking for and
perceiving the affordances for learning. As face-to-face contact with an expert
offered more opportunities for learning, hence being more salient to the
respondent, it was perceived and prioritised. Self-regulated learning offered
by timetabled activities (e.g., planning, revising, reflecting) does not provide
such salient opportunities, and is thus more challenging to be perceived and
less frequently taken up by respondents.
The next two comments illustrate the willingness of learning from
experts in face-to-face contact, and potential reasons for this preference:
156 Findings
I really feel that face-to-face contact is more focusing than sitting at
home and watching the video (Respondent 1, Focus Group 1).
I always find I learn much better when someone is explaining it to
me. More so than if I were just to read it of the textbook. It doesn’t
stick with me as well as someone is teaching (Respondent 2, Focus
Group 1).
Both respondents indicated a preference for learning from/with an expert,
experience that enhanced the knowledge assimilation through focusing,
explaining and teaching. While learning from/ with experts is highly
recommended, it appears that an overall functional context can enable
exploitation of this form of learning. The following comment made by
Respondent 2 in Focus Group 2 illustrates this:
I learn mainly socially. […] I talk to my friends, I try to befriend
people that are more ahead of me. I try to get people, I try to get one-
on-one teaching. Like, instead of looking up at lecture recordings or
instead of going to lectures because it’s so general and so broad
targeted at everyone’s different skills groups, I generally look for
students to teach me or, you know, look for connections that can
teach me face-to-face and build up social connection. […] That’s
became my main strategy now. So all I have to do is just talk to
people, even if that’s something like that maths, I still have to have
a discussion with someone. (Respondent 2, Focus Group 2)
I always make sure that I have at least five friends who have done
the unit before, one semester before me. So that, when I do the unit
I always can find sources to check up on. And then I look at the
similarities between them. They’ve already got feedback. You see,
it’s just about what kind of peers are you looking for. If you’re
looking for current, contemporary peers, you’ve got to be more
Findings 157
cautious. But if you’re looking for past peers, there is stuff that is
already graded. (Respondent 2, Focus Group 2)
Interestingly, in the above-quoted example, the status of an expert is attributed to peers
who belong to the respondents’ meso-system (i.e., fellow university students rather
than peers). Their expert status comes from successfully completing the unit one
semester earlier, as opposed to proven achievements in content knowledge. This raises
questions around learning design, in particular task and assessment design, which are
discussed further in Chapter 5.
Findings from the focus group data confirmed the questionnaire finding that
some respondents were lacking strategies to become self-regulated learners. The
following quotation illustrates typical responses from this group of students:
[Respondent 2]: If I’m not finding that stuff interesting and then I
don’t have the drive to study it beyond what’s recommended.
[Investigator]: So what do you do if there is something not
interesting?
[Respondent 2]: I will have to force myself to study and learn.[…]
It’s pretty much like nights before the test and stuff like that.
(Respondent 2, Focus Group 4).
In summary, the data analysis suggests that within the particular context of this
study, the timetabled activities (i.e. ideas), did facilitate perception and uptake of
opportunities for learning, but to a limited extent. It also appears that student
respondents’ perceptions were primarily focused on learning from/ with experts and
indicated low levels of students’ learning strategies that would enable them to become
more self-regulated and self-directed learners through, for example, collaborative
learning with peers.
From the perspective of Bronfenbrenner’s Bioecological Model of Human
Development (2005; Bronfenbrenner & Morris, 2006), the identified pattern of
perceived importance and intensity of uptake of affordances might indicate that
respondents, within formal learning networks, would first turn towards experts,
members of the respondents’ mesosystem, and next to their peers, members of their
microsystem. This observation might also illustrate how the functional context
158 Findings
(timetabled activities) triggers the uptake of learning affordances, including the ways
other social agents, members of formal learning networks, are perceived and
approached for learning purposes (for further discussion, see Chapter 5).
The next part of the questionnaire investigated how the potential for learning
offered by different social agents constituting learning networks was perceived and
taken up by respondents.
4.1.2.2 People
Eight categories of social agents, constituents of formal and informal learning
networks were identified to be investigated in this part of the questionnaire. First, the
same distinction between an expert and a peer, inspired by Bronfenbrenner’s EST
(1977; 1986; 1994; 2005) was used to develop typology for this element of learning
network. Next, the typology was further developed in attempt to identify a large
spectrum of social agents, belonging to different ecological systems and operating
within different types of learning networks. The final version of the typology was
subjected to consultation with the supervisory team, other academics and volunteering
students, as described in section 3.5.
4.1.2.2.1 Typology of learning affordances and corresponding intended
interactions
The opportunities for interaction offered by the social agents, the providers of
affordances, were defined and mapped against types of learning networks and social
agents’ categories. Table 4.9 summarises the classification of social agents and maps
them against the type of learning network and intended interaction.
Table 4.9: Identified categories of social agents, types of learning networks and intended interactions.
Social agent Type of network Intended interactionUniversity teaching staff (academics, casual academics teaching the unit under investigation)
Formal Learning from/ with experts
University support staff Formal Learning from/ with expertsFellow students enrolled in this unit Formal Learning from/ with peersStudents from other universities Informal Learning from/ with peersTeaching staff from other universities Informal Learning from/ with expertsProfessionals Informal Learning from/ with experts
Findings 159
Private tutors Informal Learning from/ with expertsFamily member (not professional or from the university)
Informal Learning from/ with experts
A series of questions enquired about the relationships between the affordances offered
by diverse social agents, and the interaction facilitated by the perception and uptake of
those affordances. The four variables included in the investigation were: importance,
frequency, sequence, and intensity of uptake of affordances. As this was a complex
question, additional questions aimed to investigate the type of social agents contacted
for assistance with learning, and the type of interactions the respondents were entering
into when contacting other social agents. As the statistical analysis provided evidence
of a high likelihood of both units being different (see p values in Table 4.9), the two
units were investigated separately.
Response rates were high for most questions, in both units. Questions enquiring
about social agents being perceived as part of the respondents’ learning networks, and
the preferred order in which respondents were asking for assistance from social agents,
delivered a 100% response from both units. For the question investigating the
importance, frequency, sequence, and intensity of uptake of affordances offered by
social agents, response rates ranged from 29% (22 out of 75) to 93% (70 of 75) in Unit
E, and from 32% (19 of 61) to 68% (51 of 60) in Unit S. A high response rate was also
observed for open-ended questions in the questionnaire with 92% (69) responding in
Unit E and 90% (54) in Unit S.
4.1.2.2.2 Uptake of learning affordances and types of enacted interactions
The study first investigated which social agents were perceived by respondents as
members of their learning networks. Table 4.10 summarises the findings from both
units, using median value in descending order.
160
Find
ings
Tabl
e 4.
10:S
umm
ary
of id
entif
ied
impo
rtanc
e, fr
eque
ncy,
sequ
ence
and
inte
nsity
of u
ptak
e of
aff
orda
nces
for l
earn
ing
offe
red
by so
cial
age
nts
in b
oth
units
.
Findings 161
Data analysis allowed for the observation of certain patterns in respondents’
perceptions. In both units, the median values of the four main variables measuring the
perceived importance, frequency, sequence and intensity of contacting social agents
were either identical or highly similar, with the difference ranging from 0.5 to 1 point.
For instance, the affordance of contacting university teaching staff (first row, position
1) received highly similar values across all variables.
The same regularity was observed across the units. The median values for the
same affordance, and across both units, were either identical or differed by only 0.5
point (first column, separate rows for each affordance). This might indicate a
consistency in respondents’ practices. That is, once the respondents perceived a social
agent as belonging to their learning network, they claimed to contact this agent
consistently, that is, with the same frequency, intensity and in the same sequence.
Data analysis also revealed that those social agents who belonged to the formal
learning network (three highest ranked) were perceived as more important, and thus
more frequently and intensely contacted than social agents belonging to informal
learning networks (the remaining five positions). The median values of the sequence
of uptake also suggest that once the affordance was perceived as important, it would
be taken up first. Within formal learning networks, respondents reported that they
perceived peers and classmates, i.e. social agents who are part of their mesosystem, as
more important, and hence contacted them more frequently and more intensely. Within
informal learning networks, family members (i.e., experts), who are also members of
the respondents’ mesosystem, were perceived as more important, followed by social
agents belonging to their exosystem (i.e., teaching staff from other universities,
students from other universities, professionals, private tutors).
This finding was confirmed by an analysis of answers to the question about
preferred order (sequence) of contacting social agents for assistance with learning
outside contact hours, and the intended type of interaction participants expected to
enter into. In addition, the data analysis indicated more nuanced information about the
preferred type of interaction facilitated by different types of social agents. As the
statistical analysis did not show any likelihood of a difference between the two units,
and the median values were identical across all affordances and in both units, the
findings have been summarised in one table (Table 4.11).
162 Findings
Table 4.11: The sequence of contacting social agents for assistance with learning outside contact hours in both units mapped against the type of intended interaction.
Legend: Colour coding of intended interactions
Type of network Colour Intended interaction Colour
Formal Yellow Learning from Blue Learning with LilacAn expert PinkA peer Orange
Informal Green
When asking for assistance with learning, student respondents in both units
stated that they would turn in the first instance to social agents belonging to their
formal learning networks, and next to their informal learning networks. Furthermore,
learning from/ with peers (i.e., fellow students) was reported as the preferred form of
asking for assistance. This finding confirms data analysis results discussed in section
4.1.2.1.2, which reported on the pragmatic attitude of respondents who, within formal
settings, preferred to contact their peers first for assistance in learning before
SEQUENCE
Ranking Type of social agent Group n Median
(8-1)
Interquartile range
25-75%
Type of the network
Intended interaction
1 Fellow students enrolled in thisunit
Unit E 69 8 8-8 Formal Learning from/ withpeers
Unit S 48 8 7-8
2 QUT teaching staff
Unit E 61 7 6-7 Formal Learning from/ with experts
Unit S 47 6 5-7
3 QUT support staff (e.g. STIMulate, Library)
Unit E 47 6 5-7 Formal Learning from/ with experts
Unit S 46 6 5-7
4 Family member Unit E 45 6 2-7 Informal Learning from/ with experts
Unit S 38 5 1-7
5 Students from other Universities
Unit E 44 5 4.25-7 Informal Learning from/ with peers
Unit S 37 5 4-6
6 Teaching stafffrom other Universities
Unit E 33 3 2-3.5 Informal Learning from/ with experts
Unit S 30 3 1-4
7 Professionals Unit E 33 2 2-3.5 Informal Learning from/ with experts
Unit S 31 3 2-3
8 Private tutors Unit E 32 2 1-3.75 Informal Learning from/ with experts
Unit S 33 2 1-4
Findings 163
contacting experts (regardless of the motivation of learning from, or just taking
advantage of, peers). Interestingly, the second affordance to learn from/ with peers
(students from other universities), part of an informal learning network, was ranked
fifth. This suggests, again, that students were focusing on learning opportunities
offered by their formal learning networks and did not take advantage of the opportunity
for self-directed learning by learning with their peers from outside their formal
network.
Learning from/ with experts was characterised by a similar regularity.
Respondents reported first contacting experts, members of their formal networks (i.e.,
university teaching and support staff). Family members, who are part of students’
informal learning networks, were then contacted next. In their open-ended comments,
some respondents explained that their family members played the role of “expert” as
they had either an appropriate education level or had completed a similar/ the same
unit in previous semesters (e.g. older siblings). Lastly, experts such as teaching staff
from other universities, professionals, and private tutors would be contacted.
A pattern might be observed based on students’ self-reported preferences to
contact social agents with a request for assistance with learning. The sequence of
contacting social agents might be related to the distinction between formal and
informal learning networks first, and then to the status of an expert or a peer, and
finally to the ecological systems to which one belongs. That is, it appears that student
respondents would turn in the first instance to members of their formal learning
networks, i.e. peers or fellow classmates, who belong to their micro-/ mesosystem.
Next, the respondents would turn for assistance to members of formal learning
networks, i.e. experts, who belong to their mesosystem (e.g., university teaching staff,
support staff). Students would then turn to members of informal learning networks,
also experts, but belonging to their microsystem (i.e., family members), and members
of their mesosystem (i.e., students from other universities and teaching staff from other
universities). Student respondents indicated they would lastly turn for assistance to
members of informal learning networks, who belong to their exosystems (i.e.,
professionals, private tutors).
As “fellow students enrolled in the unit” were reported to be contacted in the
first instance, the next section investigated respondents’ interactions with this category
of social agents to explore the nature of relationships between respondents and other
164 Findings
students and whether there was any particular order in which respondents were
entering into these relationships.
4.1.2.2.3 Interacting with peers
The study investigated respondents’ strategies of providing assistance (or not) with
learning to their fellow students enrolled in this unit. Assisting others with learning is
a good indicator of the nature of interactions respondents were entering into with each
other and their environment (e.g., learning with vs learning from) and the reasons
underpinning their actions.
An in-depth content analysis of responses of those students who provided
assistance with learning revealed three overarching categories of reasons for their
actions: 1) mateship; 2) pragmatism; and 3) self-training. Of all three categories,
mateship was most frequently cited. Some complex responses were classified in mixed
categories, such as mateship/ pragmatism, but they were less frequent.
The responses classified in the mateship category emphasised friendship, a
feeling of belonging and shared difficulty. The following is a typical example of such
statements: “Sometimes friends will ask me questions, and I will answer them because
they don't understand - and everyone (including me) is in a same boat sometimes”. It
appears that respondents assisted in the first instance their “mates”, close peers who
can be classified as belonging to respondents’ microsystem. The main reason for
assisting close peers was the feeling of “mateship”, a sense of camaraderie caused by
“being a friend” and “being in the same boat”.
The second category, pragmatism, (i.e., return of favour) suggested a
contractual relationship. The following quotation is a typical example of statements
classified in this category: “Occasionally I help other students as I hope for
reciprocation when I require it”. This statement suggests respondents’ more distant
relationship with their fellow students who, most probably, would not be perceived as
belonging to respondents’ microsystem. In short, it appears that these respondents
perceived their fellow students as members of their mesosytem (thus, members of the
broader environment).
The third category, self-training or learning by teaching others, made salient
another type of relationship, self-reflection, which indicated respondents’ being in the
process of developing self-regulation. Typical examples of respondents’ statements
classified in the third category include: “Sharing knowledge can help myself to learn”
Findings 165
or “Yes, because it helps me understand even more”. As the nature of the relationship
with other students is not mentioned, the responses did not indicate which system other
students belonged to.
The reasons for not assisting other students with learning were classified in two
overarching categories: 1) lack of confidence; and 2) low engagement with classmates;
both receiving a similar number of responses. Typically, for the first category
respondents would write: “I'm not good enough at the content, still learning” or “I’m
a student”. This indicates the low level of respondents’ self-reflection, suggesting a
low level of self-regulation, especially when adopting an agentic approach to identify
learning needs and engage in interactions with other learners to diagnose one’s actual
level of disciplinary knowledge.
As for the second category, typical examples of respondents’ claims include:
“No contact towards students in my course”, or “I've never been asked for help”. The
responses indicate collegial relationships between respondents and other students,
suggesting classmate-type relationships (as opposed to close peers). Hence, it seems
that the act of not providing assistance in learning was affecting those social agents
who belonged to the respondents’ mesosystem.
The relationship between belonging to one of ecological systems and the type
of social learning occurring within the systems was confirmed by the analysis of the
focus groups. The following comment from one of the respondents makes salient the
importance of belonging to one of the ecosystems when making effective connections
that enable co-construction of knowledge and that make learning networks productive:
It needs to be, …. you need to understand and trust the people with
your group you’re in with. (Respondent 2, Focus Group 1)
Trust is an inherent element of becoming close peers, and provides
foundations for effective (i.e., productive) social learning, as described by
another focus group participant:
I also have the mates down the hall where I live and they also study
engineering. So, we usually work as a group with regards to more
complex … like aspects … in engineering. I think it makes it a whole
lot more stomachable where you kind of bouncing ideas of and
166 Findings
another is bouncing what he knows of… and suddenly you go of one
person’s knowledge of the subject to five different people’s
knowledge of the same subject. (Respondent 2, Focus Group 4)
The respondent used the term “mate” in relation to their friends who live in the same
building as the respondent. Such proximity is favourable to build a sense of
camaraderie that encourages not only socialising but also social learning with close
peers.
In summary, it appears that respondents were prone to assist their close peers
(mates), and at the same time members of their formal learning network, in the first
instance. In the second instance, they assisted their classmates, who are also members
of their formal learning networks. While in the former category the decision was
underpinned by respondents’ feelings of mateship and willingness to assist their
friends, in the latter case it was pragmatics that motivated their actions. Furthermore,
it also seems that not being a close friend, “a mate”, reduced the opportunity of being
provided with assistance in learning by respondents.
Considered from the perspective of the architecture of productive learning
networks, it appears that the attribute of productivity, that is, facilitating interaction
and co-creating knowledge, was somehow related to which of Bronfenbrenner’s
ecological systems (1977; 1986; 1994; 2005) the others belonged to. This finding is
discussed in more detail in Chapter 5.
The next section investigated the perception and uptake of learning affordances
offered by tools, another element of learning networks.
4.1.2.3 Tools
There are many digital (e.g., online learning platforms, eBooks), analogue objects
(e.g., books), or electronic devices (e.g., laptops, mobile phones) that can be classified
as learning tools. Tools, in this study, are understood as textbooks (e. g., paper-based
hard copies, eBooks and/ or digitalised textbooks), as well as all types of computer
devices (e.g., desktop, laptop, mobile phones) and digital tools available online such
as software (e.g., applications), learning platforms or websites.
This section first briefly describes the ways electronic devices and textbooks
(including eBooks) were used by respondents, and then presents the results of an in-
depth inductive and deductive analysis of the ways online resources were used, and
Findings 167
how the affordances offered by online tools were perceived and taken up (or not) by
respondents.
4.1.2.3.1 Electronic devices
In this study, the term electronic devices encompasses desktop computers, laptops,
tablets and mobile phones, that is, the electronic devices that are most widely available
and most frequently used for learning purposes. Considering the fact that electronic
devices (at least some of them) are usually owned by users, they were classified as
elements of informal learning networks. Thus, the study investigated which electronic
devices, elements of respondents’ informal learning networks, were assisting
respondents with their learning outside face-to-face contact hours.
Response rates were high and reached 100 % of respondents in both units. All
respondents indicated that they owned at least two devices, with mobile phones and
laptops being predominantly used for learning. As for previous elements of learning
networks, the median values of importance, frequency and intensity of uptake were
calculated, alongside with interquartile ranges and p values. The findings are
summarised in Table 4.12.
168
Find
ings
Tabl
e 4.
12:S
umm
ary
of re
spon
dent
s’ p
erce
ptio
ns o
f whi
ch e
lect
roni
c de
vice
s wer
e as
sist
ing
them
with
lear
ning
, bot
h un
its. M
ultip
le re
spon
ses
wer
e al
low
ed.
DE
VIC
ES
IMPO
RTA
NC
EFR
EQ
UE
NC
YIN
TE
NSI
TY
Typ
e of
the
devi
ceG
roup
nM
edia
n(4
-1)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5%
leve
ln
Med
ian
(4-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5%
leve
ln
Med
ian
(4-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5%
leve
lY
our d
eskt
op
com
pute
r U
nit E
443
2-4
0.92
249
31.
5-4
483
2-4
0.16
9U
nit S
303
2-4
372
2-3
0.07
037
22-
3Y
our l
apto
pU
nit E
694
4-4
0.93
668
43.
25-4
* 0.
029
674
4-4
0.09
1U
nit S
494
4-4
484
4-4
464
4-4
You
r tab
let
Uni
t E26
21-
30.
162
362
1-2
* 0.
040
352
1-2
0.06
9U
nit S
231
1-3
321
1-2
311
1-2
You
r mob
ile p
hone
U
nit E
693
2-3
0.46
470
32-
30.
660
693
2-3
0.71
8U
nit S
383
2-3
433
2-3
413
2-3
Findings 169
The data analysis revealed that the median values of perceived importance,
frequency, and intensity of use of the electronic devices were identical across all
affordances in both units. Considering the median value and interquartile range, it was
appears that laptops and mobile phones were perceived as the most important and most
frequently and intensely used for learning purposes, followed by desktop computers
and tablets.
Overall, the data analysis indicated respondents’ strong preference towards
devices that are small, powerful, and portable with connection to the Internet, over
bigger devices that cannot be carried around (i.e. desktop computers). However, these
attributes do not appear to determine respondents’ selection of devices used. Tablets,
for instance, although having all three above-mentioned attributes, were the least often
owned and used by respondents. As tablets have less powerful hard drives and smaller
Random Access Memory (RAM) (system memory), this probably have prevented
some compulsory software programs required by the degree to be installed, and in this
way promoted laptop over tablets. This observation is confirmed by responses to open-
ended questions and comments. For instance, one student wrote: “Certain programs
(MATLAB) require a computer to run, hence laptop is the most useful”, while another
one stated: “Portable, powerful, efficient. Accessing resources, word processing”.
From the perspective of learning networks, the criteria of portability and
memory power should be taken into consideration by learning design teams, as it has
practical implications for designing the learning tasks (e.g., enabling learning
anywhere), supporting interactions and promoting connectedness.
The next type of tools identified as parts of respondents’ learning networks
were textbooks and eBooks. The importance of investigating the perception and uptake
of this type of tool rested with the potential of autonomous learning they offered to
respondents who wanted to use textbooks/ eBooks outside contact hours. The responses
to this question provided insight into respondents’ development of self-direction when
reaching for additional learning resources.
4.1.2.3.2 Textbooks and eBooks
The textbooks and eBooks can be classified as belonging to both types of learning
networks, formal and informal, depending on the unit’s requirements and the type of
learning resources offered on LMS platforms. The response rates to questions were
170 Findings
high for questions investigating the usage of both types of textbooks, across both units,
and reached between 94% (70 of 75) and 87% (65 of 75 respondents) in Unit E. For
Unit S, the numbers were between 90% (54 of 60) and 88% (53 of 60 respondents).
In both units, the data revealed the same pattern of responses indicating that a
large part of respondents were using both types of textbooks outside contact hours.
That is, paper-based textbooks were used by 40% (28 of 70) of respondents in Unit E
and 35% (19 of 54) in Unit S, while eBooks were used by 30% (20 of 65) of
respondents in Unit E and 45% (24 of 53) of respondents in Unit S.
An in-depth data analysis led to the construction of three overarching
categories of reasons for using textbooks (both types): 1) usefulness and/ or
convenience; 2) habit of owning a textbook/ an eBook, and 3) requirement (by
academic staff). In terms of reasons for not using these tools, respondents’ answers
were similar in both units and for both types of tools. Five overarching categories were
identified: 1) using digital/ online resources; 2) cost of the textbooks; 3) lack of habit
of using a textbook; 4) lack of requirement from academic staff, and 5) inconvenient,
not useful. In addition, three mixed categories were also identified: 1) cost/
accessibility of online information; 2) not required by academic staff/ using other
resources; and 3) cost/ perceived lack of usefulness of the tool.
Usefulness and convenience of the tool were cited most often in both units as
the reasons for either using or not using paper-based textbooks and eBooks.
Interestingly, also in both units, respondents who stated not using either both tools or
only one of them also stated using online resources as an alternative to the textbook.
For example, one respondent stated: “All the resources are available in class and on
Blackboard”, while another one wrote: “Too dense, easier to Google a topic”. It
appears that respondents are shifting their attention towards online learning resources,
often provided via learning platforms. The following quotation illustrates this trend:
“Everything online is FREE. I use them only for cross-referencing online resources”.
Such a statement raises concerns about student respondents’ skills to
appropriately evaluate the nature and quality of the online resources, and broadly, the
ways respondents were using the online resources. The next series of questions
therefore investigated the perception and uptake of affordances offered by online
resources.
Findings 171
4.1.2.3.3 Typology of online learning tools
In this study, online resources are understood to be digital learning tools, available
online and either recommended by academic teachers (e.g., learning platforms with
links to websites, applications), hence belonging to formal learning networks, or not
recommended, in which case they belong to informal learning networks. Some of these
digital learning tools are used to provide learning affordances to students (e.g.,
Blackboard), while others (e.g., specialised blogs, chat rooms, specialised websites)
address a broader audience, requiring respondents to firstly perceive learning
affordances, realise their potential, and then take them up. This requires a high level
of autonomy in learning, in the form of taking an initiative, formulating one’s learning
needs, defining objectives, strategising the ways of using the tools, and evaluating the
outcomes of the learning process. In short, some of these tools require a higher level
of self-direction (Knowles, 1975), a concept discussed in section 2.9.1.
The online learning tools have a double role in promoting interaction. On the
one hand they are connectors, facilitating interaction between social agents who use
them (e.g., via chat rooms, blogs, Facebook pages). On the other hand, some online
tools enable interaction with the tool itself, for example via drill activities with
immediate feedback (e.g., online quizzes).
The identified online learning tools were therefore mapped against the type of
learning network, the intended level of autonomy, a leading attribute to self-directed
learning, and the intended type of interaction they facilitated. The classification of the
intended autonomy ranged from tools that required limited to high levels of autonomy.
That is, online tools embedded within the LMS platforms, which are part of formal
learning networks, were classified as requiring a limited level of autonomy. They were
pre-selected for students, provided on the learning platform, often accompanied with
instructions about how to use them for educational purposes (e.g., which online journal
article to read, which podcast to listen to, etc.). The tools that were part of either formal
or informal learning networks (e.g., additional resources), depending on functional
context, were classified as requiring limited to medium levels of autonomy. Finally,
the tools that were part of informal learning networks (e.g., personal Facebook pages9)
9 Although the contributions made on Facebook (either students’ private or set-up by Unit Coordinators), should be considered as an important part of artefacts created by students, the decision was taken to not to investigate these artefacts. There were two main reasons for the decision. First, it
172 Findings
were classified as requiring high levels of autonomy. They could have been used for
learning, but their main objective was not educational.
The intended type of interaction (for learning), ranged from a lack of
interaction with the tool, or only knowledge assimilation (e.g., You Tube), through to
limited interaction with the tool if enabled and/ or required (e.g., via online quizzes),
and to interaction with other social agents (e.g., Facebook) with a focus on content
knowledge assimilation and/or production. Table 4.13 summarises the mapping of this
classification.
would be very difficult to access the data (students’ private sites), Second, the was a risk of breaching trust established between the students and the tutors who used the unit-specific sites as the medium for providing advice on assessment. Tutors took the role of site moderators as a part of a broader strategy that aimed at ensuring academic integrity of the unit.
Find
ings
173
Tabl
e 4.
13:I
dent
ified
onl
ine
lear
ning
tool
s map
ped
agai
nst t
ypes
of l
earn
ing
netw
orks
, int
ende
d le
vel o
f aut
onom
y an
d in
tend
ed ty
pe o
f in
tera
ctio
n.
Iden
tifie
d on
line
lear
ning
tool
sT
ype
of th
e ne
twor
kIn
tend
ed le
vel o
f aut
onom
yIn
tend
ed ty
pe o
f int
erac
tion
Uni
t Bla
ckbo
ard
site
(inc
ludi
ng a
ll le
arni
ng
reso
urce
s and
doc
umen
ts a
cces
sibl
e in
di
gita
lised
form
at)
Form
alLi
mite
d (d
epen
ding
on
func
tiona
l co
ntex
t and
task
des
ign)
Lim
ited
inte
ract
ion
with
the
tool
, if e
nabl
ed
and/
or r
equi
red
(e.g
., vi
a o
nlin
e qu
izze
s),
focu
s on
cont
ent k
now
ledg
e as
sim
ilatio
n
QU
T Li
brar
y si
teFo
rmal
Lim
ited
to m
ediu
m(d
epen
ding
on
func
tiona
l con
text
and
ta
sk d
esig
n)
Lim
ited
inte
ract
ion
with
the
tool
(if e
nabl
ed),
focu
s on
cont
ent k
now
ledg
e as
sim
ilatio
n
Spec
ialis
ed In
tern
et w
ebsi
tes (
e.g.
, jou
rnal
w
ebsi
tes,
othe
r uni
vers
ities
)Fo
rmal
(if r
ecom
men
ded)
an
d In
form
alLi
mite
d to
med
ium
(dep
endi
ng o
n fu
nctio
nal c
onte
xt a
nd
task
des
ign)
Lim
ited
inte
ract
ion
with
the
tool
(if e
nabl
ed),
focu
s on
cont
ent k
now
ledg
e as
sim
ilatio
n
This
uni
t’s F
aceb
ook
Form
alLi
mite
d (d
epen
ding
on
func
tiona
l co
ntex
t and
task
des
ign)
Inte
ract
ion
with
oth
er so
cial
age
nts:
cl
assm
ates
, aca
dem
ic te
achi
ng st
aff
Res
pond
ent’s
per
sona
l Fac
eboo
kIn
form
alH
igh
Inte
ract
ion
with
oth
er so
cial
age
nts:
pee
rs,
fam
ily m
embe
rs, f
riend
s Sp
ecia
lised
blo
gsFo
rmal
(if r
ecom
men
ded)
an
d In
form
alLi
mite
d to
med
ium
(dep
endi
ng o
n fu
nctio
nal c
onte
xt a
nd
task
des
ign)
Lim
ited
inte
ract
ion
with
oth
er so
cial
age
nts
expe
rts/ l
earn
ers,
focu
s on
cont
ent
know
ledg
e as
simila
tion
/ pro
duct
ion
Spec
ialis
ed c
hat r
oom
sFo
rmal
(if r
ecom
men
ded)
an
d in
form
alLi
mite
d to
med
ium
(dep
endi
ng o
n fu
nctio
nal c
onte
xt a
nd
task
des
ign)
Inte
ract
ion
with
oth
er so
cial
age
nts,
expe
rts/
lear
ners
, foc
us o
n co
nten
t kno
wle
dge
prod
uctio
n/ a
ssim
ilatio
nY
ou T
ube
Form
al (i
f rec
omm
ende
d)
and
Info
rmal
Lim
ited
to m
ediu
m(d
epen
ding
on
func
tiona
l co
ntex
t and
task
des
ign)
Lim
ited
inte
ract
ion
with
the
tool
(if e
nabl
ed),
focu
s on
cont
ent k
now
ledg
e as
sim
ilatio
n
174
Find
ings
Kha
n A
cade
my
Form
al (i
f rec
omm
ende
d)
and
info
rmal
Lim
ited
to m
ediu
m(d
epen
ding
on
func
tiona
l co
ntex
t and
task
des
ign)
No
inte
ract
ion
with
the
tool
, foc
us o
n co
nten
t kn
owle
dge
assim
ilatio
n
Inte
rnet
(gen
eral
acc
ess t
o th
e In
tern
et)
Info
rmal
Hig
hIn
tera
ctio
n w
ith th
e to
ol d
epen
ds o
n th
e in
tent
of u
sing
it
Mob
ile p
hone
app
sIn
form
alH
igh
Lim
ited
inte
ract
ion
with
the
tool
, if e
nabl
ed
and/
or r
equi
red
(e.g
. via
onl
ine
quiz
zes)
, fo
cus o
n co
nten
t kno
wle
dge
assi
mila
tion
Soci
al m
edia
(e.g
., Tw
itter
)In
form
alH
igh
Inte
ract
ion
with
oth
er so
cial
age
nts:
pee
rs,
fam
ily m
embe
rs, f
riend
s and
oth
er e
xper
ts/
lear
ners
(dep
endi
ng o
n th
e st
rate
gy o
f usi
ng
it)
Prof
essi
onal
net
wor
k co
mm
uniti
es (e
.g.
Ope
n So
urce
)In
form
alH
igh
Inte
ract
ion
with
oth
er so
cial
age
nts,
cont
ent
expe
rts su
ch a
s pro
fess
iona
ls
MO
OC
sIn
form
alH
igh
Lim
ited
inte
ract
ion
with
the
tool
and
oth
er
soci
al a
gent
s (e.
g., e
duca
tors
, oth
er le
arne
rs),
focu
s on
cont
ent k
now
led g
e as
sim
ilatio
n
Findings 175
4.1.2.3.3.1 Uptake of online learning tools
The participants’ overall response rate in this series of questions was high and
depended on the nature of the question. For instance, questions investigating the
composition of the learning networks reached 100% in Unit E and 98% in Unit S,
while more complex questions investigating perception, importance, frequency and
intensity of uptake of affordances offered by online learning tools had a broader range
of responses that stretched between 93% (70 of 75 respondents) and 19% (14 of 75) of
responses in Unit E, and between 88% (53 of 60 respondents) and 18% (11 of 60) of
responses in Unit S. For open-ended comments, the response rate also varied between
87% (65) of the respondents in Unit E and 83% (50) of the respondents in Unit S. Most
responses, however, in both units, were limited to a simple “No, I don’t”, without
further comments. The answers containing comments were less frequent and reached
only 11% (8 of 75) of responses in Unit E and 15% (9 of 60) of responses in Unit S.
First the perception of the affordance was investigated, measured against
importance, frequency, sequence and intensity of uptake. Table 4.14 summarises the
findings.
176
Find
ings
Tabl
e 4.
14:S
umm
ary
of id
entif
ied
impo
rtanc
e, fr
eque
ncy,
sequ
ence
and
inte
nsity
of u
ptak
e of
aff
orda
nces
for l
earn
ing
offe
red
by so
cial
age
nts
in b
oth
units
.
Findings 177
Contrary to the results of investigation conducted so far that showed a similarity across
all median values of measures of uptake (importance, frequency, sequence and
intensity), the data of the online tools revealed seven affordances that showed the
differences bigger than previously observed. The difference has been observed across
the units, (e.g., professional network communities, MOOCs, and mobile phone apps)
and across the values themselves (e.g., specialised chat rooms, professional network
communities, MOOCs, mobile phone apps). For these affordances, some of the
differences in median values are greater than 2 points, in both units and across at least
three measures. This suggest that, for these online tools, some of the affordances that
were perceived as important, were not necessarily taken up frequently and/or intensely.
Next a ranking of perceived affordances was established, taking as the
reference the median of importance, as the dominant indicator of perception (however
not uptake) of affordances. The ranking mapped the affordance against type of
network, intended level of autonomy and intended type of interaction. Table 4.15
summarises the results. As in previous analyses, the data were colour coded to
facilitate the process of constructing the patterns. Formal learning networks were
colour coded in yellow, informal in turquoise and formal/informal in green. As for
intended types of autonomy, limited autonomy was colour coded in yellow, informal
in turquoise and limited to medium level of autonomy in green. The differences
between the units are marked in purple.
178
Find
ings
Tabl
e 4.
15:O
rder
of u
se, p
erce
ptio
n of
the
onlin
e to
ols,
thei
r im
porta
nce,
freq
uenc
y an
d in
tens
ity o
f use
map
ped
agai
nst i
nten
ded
leve
l of
auto
nom
y an
d th
e ty
pe o
f lea
rnin
g ne
twor
k th
ey b
elon
g to
.y
yg
yg
Findings 179
Based on the data analysis, a pattern of perceptions of the affordances can be
constructed, depending on the type of learning network, the level of intended
autonomy, and the order of using the tool. The online tools facilitate interaction, and
their application for learning purposes depends to some extent on whether the
educational institution uses them as part of a Learning Management System (e/g., LMS
Blackboard). For other online tools, it depends on an academic’s decision to
recommend the tool or not. Formal online tools, such as those embedded in an LMS,
required limited autonomy. Recommended online tools implied low levels of
autonomy and were often part of respondents’ formal learning networks, while those
not recommended implied high levels of autonomy and were part of respondents’
informal learning networks. The type of intended interaction did not seem to impact
on the use of the online tools. For instance, the same intended interaction can be offered
by online tools that belong to formal and informal learning networks (e.g., LMS site,
library site, or mobile phone apps), with varying levels of autonomy. The use of these
tools did not seem to depend on the type of interaction either, but rather on the type of
network they belonged to and the level of autonomy required from their users (see
ranks 1, 4, 12).
Furthermore, in both units respondents were using, in the first instance, online
tools that were part of their formal learning networks, and that required limited or
limited to medium levels of autonomy, depending on the functional context. Next,
respondents in both units were using elements belonging to either their formal or
informal learning networks (e.g., library sites, specialised web sites), which required a
limited to medium level of autonomy. In transitioning from formal to informal learning
networks, respondents reported using tools that required a higher level of autonomy in
the final instance. This finding confirms students respondents’ indicated preference to
interact with social agents (experts vs peers), depending on the type of learning
networks they belonged to. They preferred using elements of formal learning networks
first, followed by elements of informal learning networks.
However, in the case of online tools, there were some exceptions to the
observed pattern, which can be explained by the impact of one’s frame of reference on
the uptake of opportunities for an action. For instance, web sites (general) were
reported as being used in the second instance, in both units. This online tool required
high levels of autonomy, and was classified as belonging to an informal learning
network. The internet is a tool used every day for a large spectrum of purposes, and it
180 Findings
is an important tool to assist the development of learners’ overall digital literacy, and
thus forms an inherent part of respondents’ frames of reference. For this reason, this
tool was also seen as highly important; however, it was reported to be used less
frequently as other tools that are part of formal learning networks and recommended
by the academics / institutions, such as the LMS site (part of the unit’s functional
context).
Data analysis further revealed that respondents preferred to use tools that
belonged to the formal learning network and that required limited or limited to medium
levels of autonomy, depending on the functional context. Unit S offered a Facebook
page, which was created, monitored and mediated by academic teaching staff to assist
students with their learning, in particular with assessments (see row 5). Thus,
respondents from Unit S reported using the unit’s Facebook pages, which were
elements of formal learning networks, rather than private Facebook sites. The unit
Facebook page required a limited level of respondents’ autonomy, while respondents’
personal Facebook sites, if they were to be used for learning purposes, would require
high levels of autonomy. This finding is confirmed by respondents’ comments that
expanded on their answers. One respondent wrote: “The Facebook page is brilliant
because everyone has similar questions and most tutors are active members”. This
respondent appreciated guided information and assistance provided by experts, who
are members of formal learning networks.
Data also revealed students’ low uptake of affordances offered by tools that
required high levels of autonomy, such as professional network communities, mobile
phone apps and MOOCs. These online tools, ranked at the bottom of their preferences,
can be classified as elements of informal learning networks, requiring high levels of
autonomy.
These findings were confirmed by both in-depth inductive and deductive data
analysis of respondents’ comments and their focus group contributions. The findings
emphasised the importance of the relationship between the units’ functional contexts,
respondents’ frames of reference, and the ways online tools were perceived and used.
The analysis of respondents’ open-ended comments revealed that they
predominantly perceived Blackboard and general web sites as the two online tools
assisting them most with their learning. As one respondent remarked: “The Blackboard
site has all the learning resources with the lecture recordings, and allows for the
information to be recapped and re-implemented”. This indicates that this respondent
Findings 181
did not perceive/ feel the need to search for any additional resources. The general web
sites were used in a pragmatic way to “give [me] lots of examples to solve and learn
from”. Other tools (or platforms), such as Facebook or You Tube, were also cited but
only by a few respondents. Their comments confirmed their pragmatic attitudes
towards the tools. For instance, one respondent stated: “Google. Can see how people
get their answers and find methods/ techniques that suit me”, while another one wrote:
“You Tube, so many teaching videos there, almost everything”. Finally, when
commenting on the use of MOOCs, professional communities or mobile phone apps,
some respondents stated that they have never used these tools for learning.
This observation is confirmed by an analysis of the focus group data. Only nine
respondents from both units commented specifically on their use of Khan Academy
or, in general, You Tube videos. However, those who commented on these tools
seemed to use them less frequently, as they belonged to informal, not formally
recommended, online tools (hence, requiring higher levels of autonomy). In all the
focus groups only two respondents mentioned using Khan Academy. One remarked:
But it’s always good for me to watch the video, and I watch a couple
of different videos. So, I watch the lecture, I watch supplementary
lectures that are online. But I also go to other websites like Khan
Academy and I see what they have to say as well. (Respondent 1,
Focus Group 3)
It is evident from the respondent’s remarks that this type of online learning tool, which
is not recommended and part of an online informal learning network, was used as an
addition to the recommended tools. Hence, on the one hand, it is a highly positive
finding that students were using recommended sites, elements of their formal learning
networks. On the other hand, the fact that this was one of only two contributions made
by the students participating in focus groups demonstrates that, with some exceptions,
students were overall still lacking self-direction in selecting tools that would help them
with their learning.
In summary, respondents perceived and took up the educational affordances
offered by their online tools depending on functional context first (required or not) and
type of learning network (formal/ informal). The low uptake of affordances offered by
the last three affordances (i.e., professional network communities, mobile phone apps,
182 Findings
and MOOCs) provides evidence of student respondents’ low levels of self-directed
learning skills.
This finding confirms the results discussed in section 4.1.2.1, which are
focused on timetabled activities, and those in section 4.1.2.2, which are focused on
social agents. Respondents claimed to contact experts), who are part of their formal
learning networks (e.g. academic teaching staff), as “required” by the functional
context (i.e. being an inherent part of it in the first instance.
4.1.2.3.3.2 Contribution to the online resources
To complement the information collected so far about the respondents’ level of
autonomy when using online learning tools, a series of open-ended questions about
their contributions (or lack thereof) to the online learning resources was asked. The
objective was to investigate if the learning networks enabled respondents’ perceptions
and uptake of the learning affordances of interacting with the online resources (by
contributing with them) and, in this way, co-creating knowledge. In addition, the
responses shed light on whether respondents’ networked learning practices could be
classified as productive by feeding back new knowledge to the network.
Data analysis led to construction of two overarching categories of reasons for
contributing to online learning resources: 1) collegiality; and 2) self-training. Only
four mixed responses were provided, citing collegiality and pragmatics (expectation
of returning the favour) as the main reasons that encouraged respondents’
contributions to the online resources, as illustrated by a typical response from the first
category: “Open discussion between students, some may have similar issues that you
may have (students help one another)”, and an example of a typical response for the
second category: “Taking part in chat groups with peers helps collaborate and helps
me to examine my own understanding by helping others. If it helped me, it will help
someone else. Sharing is caring”.
As for reasons not to contribute to the online resources, the content analysis led
to identification of the following three overarching categories: 1) lack of confidence;
2) lack of cognitive engagement; and 3) pragmatics. The most often-cited reasons were
lack of confidence and lack of engagement with classmates. In relation to the first
identified category, one respondent wrote: “I do not feel I have anything to contribute”,
while another one stated: “No, I don't think I could add anything valuable”. The
Findings 183
following two examples illustrate the typical comments classified in the latter
category:
No. I don't spend unnecessary time on these resources.
There was never a chance to contribute to these activities.
Finally, the pragmatics category included the statements such as:
No, lots of effort for little reward.
No because I only use them to answer questions of my own.
The number of responses containing developed answers (as opposed to short
answers “No, I don’t”) was low and somehow confusing. In addition, many
respondents provided reasons for assisting their classmates rather than explained their
motivations for contributing to the online resources. It appears that the respondents did
not perceive either the affordances of contributing to the online resources nor the
potential benefits coming out of this activity. The second part of the question
investigating the reasons for not contributing to the online resources confirmed this
finding. Overall, respondents seemed to not perceive opportunities to develop their
active learning skills when learning by contributing to the selected online resources.
Such a contribution would require them to identify an appropriate online learning
resource, define their own learning needs as part of contributing, setting up learning
goals when preparing a contribution, and deciding on the format of the contribution.
All these steps form the concept of self-directed learning (van Meeuwen et al., 2013),
discussed in section 2.9.1.
In summary, it appears that respondents did perceive the affordance of
contributing to the online resources, but only took this up to a limited extent. Their
responses focused on elements of formal learning networks (e.g., the unit’s Facebook
page), which confirms the findings from the section 4.1.2.3 pointing towards
relationship between the type of the network (e.g. formal, informal) and the functional
context. The data further suggests that respondents might have privileged a pragmatic
approach, using elements of formal learning networks rather than investing time and/or
energy in becoming active, self-directed learners.
184 Findings
4.1.2.4 Artifacts
This section looked at the ways respondents were using their learning networks to
produce artifacts as evidence of levels of awareness of self-regulated learning
(Zimmerman & Schunk, 2011; Schunk & Greene, 2018) (section 2.9.2). Artifacts in
this study are defined as outcomes of human learning activity, such as learning
resources (e.g., handwritten notes), digital documents, and learning materials produced
by a learner, or more specifically:
4. Learning materials available online;
5. Learning notes produced electronically;
6. Learning notes made by hand.
Ways of handling artifacts, and hence interacting with them, can be seen as
indicators of becoming a self-regulated learner. Interacting with artifacts requires
planning (forethought), implementation (performance), and self-reflection in relation
to the outcome (Zimmerman, 2002). The identified ways of handling the artifacts were
therefore mapped against required levels of self-regulation. The levels of self-
regulation were defined based on the required level of planning, implementing the
planned changes (i.e., processing or producing new artifacts), and reflecting on the
outcomes. For instance, not processing the learning material at all or only downloading
it to one’s device was classified as requiring low level of self-regulation as these
activities is limited to only processing the data without intellectual investment. Making
hand-written notes and storing them in a file and/ or making electronic notes and
storing them electronically requires an important intellectual investment in form of de-
/re-constructing knowledge, planning, producing an outcome and self-reflecting on the
outcome. Finally, the last identified way of handling the artifacts, producing one’s own
learning resources requires an important intellectual investment and a sophisticated
and well implemented strategy. Table 4.16 presents the identified ways of handling the
artifacts mapped against required levels of self-regulation.
Findings 185
Table 4.16: Types of handling the artifacts mapped against required levels of self-regulation.
Ways of handling the artifacts Required level of self-regulation
Doing nothing with learning material (not processing it in any way).
Low
Downloading material to one’s devices. LowMaking hand-written notes and storing them in a file (a traditional notebook).
Medium
Making electronic notes and storing them in a digital file/ notebook.
Medium
Producing one’s own new learning resources High
4.1.2.4.1 Handling artifacts
Response rates were high and reached 93% of respondents in Unit E (70 of 75) and 96
% (58 of 60) in Unit S. Data analysis revealed almost identical patterns of handling
artifacts that the student respondents had learnt in their previous educational settings,
such as in high school, which requires medium levels of self-regulation. Table 4.17
presents a comparative analysis of respondents’ answers, number of responses, and
required levels of self-regulation.
Student respondents from both units claimed that manual processing of
resources and storing them in a file/ notebook was the most frequent type of data
handling performed by them, and in the first instance. This is a standard strategy that,
most probably, respondents had learnt in their previous educational environments. The
fact of self-reporting this strategy suggests that respondents were (at least) attempting
to plan/ planning their learning (i.e., how to make notes, where to store them), and
implementing plans (i.e., actually engaging with knowledge by making hand-written
notes). However, it does not suggest respondents’ self-reflection on the process, and is
thus classified as medium-level self-regulation. Downloading the material to one’s
device, a strategy that does not indicate self-regulation, was cited as the second most
frequent; however, the questionnaire allowed multiple answers and this strategy is thus
likely a part of more complex strategies, such as making one’s own resources.
Interestingly, producing one’s own resources was self-reported by the respondents as
the third most often used strategy which suggests that respondents were making effort
186 Findings
to diversify their learning strategies. This requires high level of self-regulation, as this
involves engagement with knowledge resulting in co-creation of knowledge, a process
that necessitates planning, implementing and self-reflecting. The question remains
whether these efforts resulted from a well-planned strategy, which would provide
evidence of respondents’ self-regulation, or was it rather effect of less planned
strategy, based on previous experiences, or on “trial and error” approach.
Student respondents from both units claimed that manual processing of
resources and storing them in a file/ notebook was often the first and the most frequent
type of data handling performed by them. This is a standard strategy that, most
probably, respondents had learnt in their previous educational environments. The self-
reporting of this strategy suggests that respondents have at least attempted to plan their
learning (i.e., how to make notes, where to store them), and implemented their plans
(i.e., actually engaging with knowledge by making hand-written notes). However, it
does not show respondents’ self-reflection on the process and is therefore classified as
medium-level self-regulation.
Downloading material to one’s device, a strategy that does not require self-
regulation, was cited as the second most frequent activity; however, the questionnaire
allowed multiple answers and this strategy is thus likely a part of more complex
strategies, such as creating one’s own resources. Interestingly, producing one’s own
resources was self-reported by the respondents as the third most often used strategy,
which suggests that respondents were making an effort to diversify their learning
strategies. This requires high levels of self-regulation, as it involves engagement with
knowledge resulting in the co-creation of knowledge, a process that necessitates
planning, implementation and self-reflection. The question remains whether these
efforts were the result of a well-planned strategy, which would provide evidence of
respondents’ self-regulation, or was it rather an effect of less planned strategies, based
on previous experience or on a “trial and error” approach.
Find
ings
187
Tabl
e 4.
17:C
ompa
rativ
e an
alys
is o
f res
pond
ents
’ ans
wer
s, nu
mbe
r of r
espo
nses
and
requ
ired
leve
ls o
f sel
f-re
gula
tion.
UN
IT E
UN
IT S
Ran
king
Typ
e of
the
activ
ityn
Req
uire
d le
vels
of
self-
regu
latio
nR
anki
ngT
ype
of th
e ac
tivity
nR
equi
red
leve
ls o
f se
lf-re
gula
tion
1M
akin
g ha
nd-w
ritte
n no
tes a
nd
stor
ing
them
in a
file
(a tr
aditi
onal
no
tebo
ok)
56M
ediu
m1
Mak
ing
hand
-writ
ten
note
s and
st
orin
g th
em in
a fi
le (a
trad
ition
al
note
book
)46
Med
ium
2D
ownl
oadi
ng m
ater
ial t
o on
e’s
devi
ces
52Lo
w2
Dow
nloa
ding
mat
eria
l to
one’
s de
vice
s44
Low
3Pr
oduc
ing
one’
s ow
n ne
w le
arni
ng
reso
urce
s39
Hig
h3
Prod
ucin
g on
e’s o
wn
new
lear
ning
re
sour
ces
33H
igh
4K
eepi
ng m
ater
ial o
nlin
e in
its
orig
inal
des
tinat
ion
34Lo
w4
Mak
ing
elec
troni
c no
tes a
nd
stor
ing
them
in a
dig
ital f
ile/
note
book
21M
ediu
m
5M
akin
g el
ectro
nic
note
s and
stor
ing
them
in a
dig
ital f
ile/ n
oteb
ook
19M
ediu
m5
Kee
ping
mat
eria
l onl
ine
in it
s or
igin
al d
estin
atio
n19
Low
6D
oing
not
hing
with
lear
ning
mat
eria
l (n
ot p
roce
ssin
g it
in a
ny w
ay)
7Lo
w6
Doi
ng n
othi
ng w
ith le
arni
ng
mat
eria
l (no
t pro
cess
ing
it in
any
w
ay)
6Lo
w
188 Findings
Section 4.1 has discussed findings in response to the Research Question 1
investigating the architecture of the presupposed learning networks based on self-
reported perceptions and uptakes of learning affordances. The next section provides
answers to Research Question 2 by investigating in detail students’ learning practices
within their presupposed learning networks.
4.2 RESEARCH QUESTION 2: WHAT NETWORKED LEARNING PRACTICES ARE OCCURING WITHIN THE PRESUPPOSEDLEARNING NETWORKS?
This research question enquired whether respondents perceived and took up the
affordances provided by learning networks to assist them in becoming autonomous
learners. That is, the question investigated to what extent respondents claimed to
consciously planning, implementing and reflecting on their learning practices, leading
to their interaction within/with learning networks, and ultimately making the networks
productive. To this end, the study explored 1) respondents’ learning environments; 2)
their studying for timetabled activities; 3) attendance at timetabled activities; 4) self-
reflection on effectiveness of learning practices; and 5) self-assessment of learning
skills.
Overall the response rate was high in both units, but varied depending on the
nature of the question, with open-ended comments receiving on average 29% of
responses. In relation to respondents’ learning environments, the overall response rate
ranged from 84% (59 out 70) in Unit E to 90% (54 of 60) in Unit S. The next series of
questions, looking at preparation for and attendance at timetabled activities, reached
between 100% for the quantitative questions in both units and 77% (46 of 60) of
respondents in Unit S and 99% (74 of 75) in Unit E. Response rates for the questions
on self-reflection on effectiveness of learning practices reached between 93% (70 of
75) of responses in Unit E, and 97% (58 of 60) of respondents in Unit S. Finally, the
response rate to questions investigating self-assessment of learning skills ranged
between 97% (58 of 60) of respondents in Unit S and 100% (75) of respondents in
Unit E.
Findings 189
4.2.1 Learning environments
First, respondents’ social and learning environments were investigated. Data analysis
revealed that, in both units, respondents were usually studying alone (41% in Unit E
and 38% in Unit S), or with a friend (16% in Unit E and 22% in Unit S). The affordance
of learning within a study group was usually not taken up, with only one respondent
per unit mentioning learning within a study group containing a minimum of three
members. With regards to the usual places where the learning took place, data
indicated that respondents were predominantly learning at home or both at home and
on-campus. Other possibilities such as learning only on campus or on public transport,
or a combination of both, received fewer responses.
Analysis of focus group data pointed towards a problem that some respondents
experienced, namely difficulties with creating an environment that would assist them
with learning. When describing their learning environment, one respondent stated:
I probably study all over the place. I don’t really have any set study
area, because I tend to lack focus and with more people in the room,
more likely I will become distracted. So, I like to move around a lot.
I use [name of the campus] quite a lot because I live just across the
road and study rooms are available. And I also think it’s kind of good
to circulate between all those places because it doesn’t feel dull and
I don’t associate boring study with one particular place. (Respondent
2, Focus Group 4)
This statement also made salient the problem of students who did not have a quiet study
place at home. As the preferred learning practice of many students is quiet study at
home, such a preference should be considered by educational institutions in both
architectural and learning design. From an architectural perspective, should
universities provide more welcoming, peaceful, and comfortable learning spaces for
students? As for learning design, it appears that the learning tasks designed for the units
under investigation did not effectively facilitate interactions within learning networks,
either formal or informal.
The next series of questions investigated further learning routines for
timetabled activities.
190 Findings
4.2.2 Studying for timetabled activities
Overall, 73% (41 of 56) of respondents in Unit S and 77% (53 of 69) of respondents
in Unit E identified that they were studying alone, at home, either before or after a
timetabled activity, using online materials. Respondents also stated that, if they needed
assistance, they would contact either their close peers, their classmates, or university
support staff. This finding confirms the results of data analysis discussed in section
4.1.2.2 about the role (or different roles) social agents (or different social agents) play
in assisting respondents with their learning.
When reporting on time spent on studying, the average time indicated by
respondents was between 6 and 10 hours per week in Unit E and between 2 to 6 hours
per week in Unit S, as illustrated in Figure 4.3 and Figure 4.4.
Figure 4.3: Self-reported time spent on studying, in hours. Unit E (n=69).
1
5 5
9
16 16
7
43 3
0
2
4
6
8
10
12
14
16
18
Num
ber o
f res
pond
ents
Reported number of hours0 1 to 2 2 to 3 3 to 6 6 to 8 8 to 10 10 to 15 16 to 20 more than 20 descriptive
Findings 191
Figure 4.4: Self-reported time spent on studying, in hours. Unit S (n=56).
When asked about estimated time, in their opinion, they should spend studying
in their respective units, the respondents seemed confused and provided a wide range
of answers from 3 to 6 and 8 to 10 in Unit E, and 3 to 6, 6 to 8 and 10 to 15 as
summarised in Figure 4.5 and Figure 4.6.
Figure 4.5: Reported number of hours per week respondents should spend on studying, Unit E, (n=74).
2
15
10 10
6
45
3
01
0
2
4
6
8
10
12
14
16
Num
ber o
f res
pond
ents
Reported number of hours0 1 to 2 2 to 3 3 to 6 6 to 8 8 to 10 10 to 15 16 to 20 more than 20 descriptive
1
5
9
20
6
18
54
3 3
0
5
10
15
20
25
Num
ber o
f res
pond
ents
Reported number of hours
0 1 to 2 2 to 3 3 to 6 6 to 8 8 to 10 10 to 15 16 to 20 more than 20 descriptive
192 Findings
Figure 4.6: Reported number of hours per week respondents should spend on studying. Unit S (n=59).
Interestingly, in both units many respondents reflected on the insufficient
amount of time they spent on weekly study for their respective units. This finding was
confirmed by analysis of open-ended questions that contained frequent statements
were: “I do not spend enough time now, I should study more”. It appears that
respondents associated time spent on studying with effectiveness in their studies. This
indicates respondents’ low levels of awareness of self-regulatory strategies and
practices, including time-management that would assist them in becoming self-
regulated learners. The following response summarises the respondents’ answers in
this respect: “I do some practice quizzes but I don't really study at the moment. If I
need help, I either ask friends in the course or family members and WHEN I need
help”. Furthermore, the lack or low levels of awareness of powerful learning strategies
was also revealed by the analysis of focus groups contributions. The following
response from a focus group respondent illustrates the problem:
I’ve been through a lot of trial and error. I mean, you can get ideas
and reading from the internet about that, but everyone learns in
different ways and not every option is going to be good for you. So,
definitely trial and error. (Respondent 2, Focus Group 6)
23
17
12
5
12
4
0
2
0
2
4
6
8
10
12
14
16
18
Num
ber o
f res
pond
ents
Reported number of hours
0 1 to 2 2 to 3 3 to 6 6 to 8 8 to 10 10 to 15 16 to 20 more than 20 descriptive
Findings 193
Lack or low awareness level of effective learning strategies is further supported by the
data analysis that revealed many respondents’ highly pragmatic attitude towards
learning, consisting of learning for assessment only. In their responses, 27% (15 of 56)
of respondents in Unit S and 45% (18 of 40) of respondents in Unit E stated studying
for assessment only.
In summary, the data analysis in this part of the study confirmed findings
related to Research Question 1, which showed students’ pragmatic attitudes towards
learning and their lack, or low levels of, awareness of powerful learning strategies.
The next section reports on results of the data analysis looking at respondents’
perceptions of the value provided by timetabled activities, demonstrated by attendance,
or non-attendance, at face-to-face, on campus learning activities.
4.2.3 Attendance/ non-attendance at the timetabled activities
First, respondents’ perception of units’ learning flow was investigated, to enquire
whether the logical connection between all elements of learning networks was salient.
The aim was to investigate whether respondents were aware of how the content was
introduced, explained, practiced through blended learning design, and how the
components of blended learning complemented each other. The findings enabled to
cast some light on the reasons why respondents were attending the timetabled
activities.
Overall, the data analysis revealed that respondents saw connections between
elements of learning networks, especially of formal learning networks. The number of
respondents who reported not perceiving such connections was low and ranged
between 4% and 10%, depending on the unit. An in-depth analysis of open comments
indicated flaws in designing learning tasks and going off topic by the teaching staff as
the main reasons preventing respondents from perceiving the learning flow. Typical
comments made by respondents included:
Respondent 1 (on lectures): Sometimes it seems to be a little bit
irrelevant.
Respondent 2 (on workshops): We went off, only briefly touch upon
the topics taught in lectures and then do only one thing (assessment for
this subject and the worksheets for the week).
194 Findings
Respondent 3 (on all three timetabled activities): There wasn't clear
connections between the content. I had a hard time making links
between the lecture and the workshop/ computer lab.
The above-cited comments pointed towards the important role learning flow
played for units’ functional context, the trigger of uptake of affordances. Learners
require clarity, consistency, and relevance of the learning flow. When these
requirements were not observed, the functional context was not clear and could not
fulfil its role as a trigger of the uptake of affordances for learning. Moreover, these
comments also made salient the importance of academics’ teaching skills and
coordination of teaching teams, so the content of each activity is complementary and
scaffolds learners towards achievement of learning objectives.
The fact of perceiving (or not) the learning flow might somehow have
influenced attendance at timetabled activities. Data revealed that out of 1165 students
enrolled in both units, 135 students filled in the questionnaire and all of them
responded to the questions specifically enquiring about students’ attendance at
timetabled activities. The data revealed that 90% of respondents in both units attended
lectures (with varying frequency). Workshops reached 87% in Unit S and 100% in
Unit E of those who attended either always or most of the time. Finally, computer
laboratories offered only in Unit S, were attended by 60% (37) of respondents either
always or most of the time. Figure 4.7 presents the details of the data analysis,
summarised for both units and all types of timetabled activities.
Findings 195
Figure 4.7: Summary of students’ attendance at all three types of timetabled activities in Unit E (n=75) and Unit S (n=60).
As explained in section 3.5, the data were collected from students who
physically attended the lectures and workshops. These findings are constistent with the
above-presented findings from research question one (section 4.1.2.1) about the uptake
of affordances offered by the timetabled learning activities. It appears that those
respondents who committed to attend timetabled activities were consistent in their
practice, with very few not attending or missing the activity. The following comment
from one of the focus groups participants illustrates this finding:
I feel like most of what I get, I get in face-to-face. And yes, part of
this is because I learn a bit better like that. The biggest benefit I get
from it is the structure … having to show up and it’s like, ‘I’m here
for two hours, in a room, I’m just going to do as much as possible
whereas at home I just have that million things I would rather do, so
many distractions. So that’s why I force myself to get on the bus,
come in four days a week, 20 hours a week, whatever, how many
contact hours. (Respondent 2, Focus Group 1)
23
42
51
40
32
1410
24
19
35
11
41
47
35
1 10
10
20
30
40
50
60
Unit S, Computerlaboratories
Unit S, workshops Unit E, workshops Unit S, lectures Unit E, lectures
Num
br o
f res
pond
ents
Always Most of the time Sometimes Occasionally Never
196 Findings
It also appears that respondents’ decisions to attend were connected to their
positive perception of the value of time to be spent on studying and timetabled
activities, as demonstrated in sections respectively 4.2.2 and 4.2.3. When learners
became aware of the benefits of timetabled activities, the importance, intensity, and
frequency of uptake of learning affordances was realised, which is why they were
attended by respondents.
The findings in this is part of the study were complemented by both an analysis
of open-ended comments and the analysis of focus groups.
An in-depth content analysis revealed four overarching categories of reasons
for attending, presented in descending order of frequency of responses: 1) learning; 2)
pragmatism; 3) learning strategy; and 4) self-awareness. Some infrequent responses
contained more than one reason and were classified as mixed categories, representing
different combinations of four identified overarching categories.
Responses in the first category (learning) indicated respondents’ focus on
knowledge acquisition, which suggests their genuine interest in the units’ content. For
instance, when commenting on attendance at the lectures, one respondent wrote: “To
gain a greater understanding of concepts & learn content”. A similar comment was
made with regards to attending workshops: “Can learn content that I didn't understand
in the lecture”, while another respondent, commenting on attendance at the computer
laboratories, stated: “To learn R and to understand content”. This demonstrated interest
in learning may also be a source of frustration for some respondents who decided to
attend face-to-face activities to gain maximum benefit of learning from experts. Yet
sometimes, this becomes challenging, as illustrated by the following quotations:
It tends to be not much time to ask too many questions in lectures. It
really does disrupt everybody else and it takes time. The lecturer is
obviously prepared for the performance to fill up whatever it is, one
hour, two hours… and there is not really room to ask the question
during the lecture. And they ask questions and I find that very few
people respond. Yeah, getting responses out of them is like pulling
teeth. (Respondent 1, Focus Group 1)
And a bit later, the same respondent added:
Findings 197
Yes, because then the time pressure takes over, because they are
under pressure to cover quite a lot of material in that. And that’s
similar for the workshops. Maybe some tutors are more open, but we
rush through it to hammer it. […] Yeah, they sometimes are just
getting to the answer and rushing through to finish the material.
(Respondent 1, Focus Group 1)
This respondent pointed towards the problem perceived by many respondents
which, potentially, can contribute to the decision of not attending the face-to-face,
timetabled activities. The respondents, apart from perceiving the benefits, also
perceived the challenges of timetabled activities, such as (limited) catalogue of
teaching methods (i.e., focus on performance), combined with the volume of content
information covered within a limited timeframe. This finding is discussed further, from
the academic teaching staff’s perspective, in section 4.3.
The questionnaire’s open-ended comments, classified in the second category
(pragmatism) of reasons for attending, ranged from advantage of staying in direct
contact with academics or peers, through to time constraints and the importance of
gaining valuable information about the assessment. For instance, with regards to
computer laboratories, one respondent wrote: “Essential for learning the
programming/ computational aspect of the subject”, while another commented about
attending the workshops: “The tutor is always willing to answer questions that you
have, and it forces you to do work so you don't have to do it at home”. The opportunity
of accessing assistance from experts was highly valued by respondents, which is
illustrated by the following comment: “They [i.e., tutors during workshops] help a lot
with PSTs to understand assessment”.
The analysis of focus group comments provided further, more developed
explanations of respondents’ practices classified in this category, as the following
quotation illustrates:
I’m coming here physically just really for the workshops or for the
labs, really to fill the gap between what I’m absorbing at home,
online and what I have been missing out on. […] Although online
is fantastic, I think there still need to be a touching base a little bit
198 Findings
at the uni. If not lectures, but maybe in workshops or labs, or
something. (Respondent 2, Focus Group 4)
The responses in the third category (learning strategy) revealed some level of
respondents’ strategic thinking with regards to their decision-making process of
attending the timetabled activities, such as planning their progress and monitoring their
learning practices, as illustrated by: “Cause I wouldn't do it at home”. This finding is
supported by the focus group analysis. For instance, one respondent remarked:
I think that attending the classes definitely gave me that perhaps…
just the kind of …. determination and the confidence, you know…
to actively teach myself: ‘Alright, I’m going to achieve this this
week’. And you know, staying on top of your work, yeah….that’s
me. (Respondent 1, Focus Group 3)
Finally, responses classified in the fourth category (self-awareness) indicated
some degree of respondents’ metacognitive awareness of their own learning. The
following comment is typical for responses classified in this category: “I learn better
and concentrate better in a classroom environment than at home”. The following focus
group quotation shows this type of thinking about their learning:
I go to lectures because I don’t have the discipline to sit at home and
watch the lecture, I also find that I absorb lot more if it’s in person
and I’m not at home being distracted, or at the Library being
distracted. I go to practical classes because I really enjoy them and I
learn a lot and it’s just fun, like experiments. Tutorials, I don’t think
I get a lot out of it. Like Science tutorials, I would go to them if they,
…., like sometimes it’s like assignment help. (Respondent 2, Focus
Group 6)
Respondents also provided reasons for not attending timetabled activities,
which were analysed and classified in four overarching categories: 1) pragmatism; 2)
learning strategy; 3) lack of learning strategy; and 4) self-study.
Findings 199
Pragmatic reasons were often related to respondents’ personal situations, such
as work commitments (for demographic data see section 3.2.3), or the choice of
spending time to commute to the university, especially when an alternative solution
was provided. For instance, one respondent remarked:” Why would I come to Uni just
for a 1 hour lecture when it's online?” As for learning strategy, some respondents
consciously decided not to attend timetabled activities. While the majority stated
learning by using provided online resources and asking “their mates” for assistance,
one participant of the focus group attracted attention to an important aspect of task
design allowing the student to not attend:
I’m one of these people who do not attend. I’m here today only
because I have 100% due this week, tomorrow. I’m not attending
because I got other things to do that’s more urgent. Like the other
extra-curricular activities. I’m not attending because…… uhm…. I
don’t know. I don’t feel the need, I’m not disciplined enough. I’m
not mature enough. (Respondent 2, Focus Group 2)
This quotation clearly illustrates how the functional context enables non-attendance
at the face-to-face activities. The same respondent later stated:
So, you need to stop, immediately, with that much flexibility
compared to high school. It’s a downfall. I think if you want to have
that much freedom at the university, you want to start approaching
that freedom at the end of grade 12, so the students don’t get like a
sudden change. They more expect it, to build up their individual
discipline. Or, you have a strict rules, just like in the high school.
(Respondent 2, Focus Group 2)
The respondent lacked self-regulation and awareness of effective learning strategies
(see also sections 4.2.6 and 4.2.7). Furthermore, a lack of learning strategies was
identified through analysis of open-ended comments: “It takes too long to follow the
lecture”, or simply “Hard to stay focused”.
Finally, some students’ responses indicated the development of their self-
study skills, which led them to take conscious decisions of not attending, based on
200 Findings
the preferences in the ways they learn. For instance, one student wrote in the open-
ended comment: “I am more comfortable with learning at home for this subject, on
my own pace”, while another focus group respondent said:
So for me to get to the university here it’s very difficult. And I have
to take off work to be here. So for me to watch my lectures online
is fantastic. And all the tutorials, and everything is online it’s just
so convenient. I mean, I’m not restricted to tuition hours. I can do it
at any time I want, as many times as I want, so…. I think online is
fantastic. (Respondent 2, Focus Group 4)
Interestingly, both respondents only mentioned their satisfaction with online learning,
without reflecting on their learning strategies. It appears that learners did not reflect
metacognitively, and they might just apply a “trial and error” strategy, as already
observed in section 4.2.2.
In summary, it appears that non-attendance did not result from perceived lack
of benefit of timetabled activities or loss of interest in learning, but was rather related
to either respondents’ frames of reference (e.g., being a student and an employee,
financial situation), or the units’ functional context enabling non-attendance (e.g.,
unclear learning flow, teaching as performing). From the perspective of learning
networks, this is an important finding, which emphasises the role of a rationale when
designing formal learning networks. To interact, social agents need to see the reason
for engaging in interaction, perceive opportunities for interaction, and be in a
supportive environment that encourages interaction. The section 4.2.4 investigated
respondents’ practices when learning opportunities provided by face-to-face activities
were missed.
4.2.4 Catching up with missed content of timetabled activities
Section 4.1.2.1 discussed respondents' uptake of diverse affordances for learning
offered by three types of timetabled activities. It was demonstrated that respondents’
interactions with the elements of learning networks, in both units and across all three
types of activities were primarily focused on learning content or learning/ practising
content, depending on the type of functional context. Complementing findings from
Findings 201
section 4.2.3 on attending/ non-attending the timetabled activities, this section
discusses respondents’ ways of catching up with missed content of the timetabled
activities.
In Unit S, 46% (27 of 59) of respondents and 39% (29 of 75) of respondents in
Unit E stated that they watched lecture recordings online, as part of their strategy of
catching up with the content of a missed lecture. In the comments explaining the
reasons for watching lecture recordings, the content analysis revealed similar patterns
in both units leading to the identification of three overarching categories of reasons for
watching the lecture recordings: 1) learning; 2) pragmatics; and 3) learning strategy.
Two factors affected the respondents’ decisions to watch the lecture
recordings: their frames of reference (e.g., personal circumstances) and the functional
context of other units. For instance, the following statement, classified in the
pragmatics category, illustrates the impact of both factors:
1. There were other assignments that needed to be finished urgently
as I prioritised them.
2. If the lecture is the only class of the day, I choose to watch it at
home instead because the travelling cost is not worth it.
This finding confirms the important role played by both frame of reference and
functional context in the uptake of opportunities for learning.
Some respondents also consciously decided to watch lecture recordings as part
of their learning strategy: “I can pause if I don't understand, rewind, slow down, speed
up”.
Reasons for not watching the online lecture recordings, and hence for not
catching up with missed content, were classified in four overarching categories: 1)
pragmatics; 2) learning strategy; 3) lack of learning strategy; and 4) self-awareness.
The pragmatics category included predominantly lack of time to watch the online
content, while the learning strategy category encompassed statements indicating that
respondents were taking strategic decisions regarding their learning practices. For
instance, one respondent wrote: “I find it easy to focus when I come to the lectures”.
The respondents’ answers, classified in the third category (lack of learning strategy),
encompassed a broad spectrum of reasons, indicating low levels of interaction with the
202 Findings
elements of the learning networks. The following comments from various respondents
illustrate this finding:
Respondent 1: Just didn't remember or want to.
Respondent 2: I have been too laid back and unengaged which I have
realised is not effective in any way.
Respondent 3: I simply was too focused on other aspects of my life and
could never be bothered to go back over lectures which I realise is bad.
These comments provide examples of, on the one hand, respondents’ low awareness
of effective learning strategies (see also sections 4.2.5, 4.2.6 and 4.2.7), and on the
other hand, functional context that did not trigger the uptake of this learning affordance
(section 4.1.2.1). That is, it appears that the need for watching the lecture recording
was not successfully created to facilitate interaction with this online resource.
Similar findings were made in relation to respondents’ strategies of catching
up with missed workshops and computer laboratories. A content analysis of students’
responses revealed three overarching categories of strategies, used for both types of
timetabled activities: 1) self-study; 2) attending other workshop/ computer laboratory;
3) contacting classmates. Self-study was the most often cited strategy, in both units
and for both types of timetabled activities.
In relation to workshops and computer laboratories, the majority of
respondents developed strategies of catching up with missed content. That is, 53% of
respondents in both units (i.e. Unit E: 40 of 75 and Unit S: 32 of 60) mentioned
catching up with the missed content of the workshops, and 42% of respondents were
catching up with missed computer laboratories. However, their strategy was often
limited to online self-study, worksheet/ software practice. This suggests that this was
perceived as appropriate and enabled by the functional context. Furthermore, it
suggests that the affordance of collaborative learning with/ from peers in a face-to-
face situation, although perceived and taken up by respondents (section 4.1.2.2), was
not critical for passing the unit.
In summary, the data analysis revealed the impact of the functional context,
and to a lesser extent of frames of reference, on respondents’ learning practices,
especially those that require a more self-regulated approach to plan, implement and
reflect on the strategies for catching up with missed content. This raises questions
Findings 203
about the learning design of the units, which allowed for non-attendance while still
passing the unit. The following fragment of a focus group discussion illustrates this:
Investigator: Do you think it is enough just sit at home and use the
resources available on the Blackboard site, learn enough to be able
to satisfy the criteria of passing the unit. Is it enough?
Respondent 2: It would be possible, yes (Focus Group 4).
The next section complements the findings of the last three Sections by investigating
respondents’ self-reflections on the effectiveness of their learning practices.
4.2.5 Self-reflection on effectiveness of learning practices
This part investigated respondents’ reflections on the effectiveness of their learning
practices in the context of a blended learning approach. The questions were aimed at
looking closer at respondents’ levels of awareness of self-regulation.
In total, 84% (49 of 58) of respondents in Unit S and 94% (66 of 70) of
respondents from Unit E considered learning at their own pace, using a blended
learning approach and their personal learning networks as an effective learning
strategy. Analysis of comments accompanying the questions identified three
overarching categories of perceived benefits of blended learning: 1) opportunity to
develop self-awareness of one’s learning practices; 2) better understanding of the
content; and 3) overall appreciation of the approach. The classification is presented in
descending order of perceived benefits for each category. That is, the benefits from the
first overarching category were the most frequently cited, followed by the benefits
from the second and third categories.
The following quotations illustrate the most typical statements for the first
identified overarching category:
Respondent 1: I know my capacity and with other commitments it
means that I am able to manage my time appropriately.
Respondent 2: It is important to enforce on an extremely controlled
schedule as students can become lazy.
Respondent 3: It's easier for me to study at my own pace but sometimes
I do need someone to chase me to do a specific task.
204 Findings
All these responses have, as a common denominator, the respondents’ self-awareness
of lacking effective learning strategies that would assist them with better planning and
implementing their learning practices. These are the first two components of self-
regulated learning; the third component, reflection, was not mentioned (hence
considered as not perceived, and not taken up) by respondents.
The second identified overarching category (better understanding of the
content) confirmed low levels of respondents’ awareness of effective learning
strategies, but in a different way. That is, some respondents were applying effective
learning strategies, without realising it. The following examples are typical for this
overarching category:
Respondent 1: You understand best when you do it yourself.
Respondent 2: Enhances my knowledge so I can have reviewed
content a few times.
Respondent 3: Practising my own helps me to do the other content,
which helps overall.
These examples suggest that the respondents were in the process of becoming self-
regulated learners, but it appears that they were not aware of this.
Finally, the third overarching category (overall appreciation of the approach) is
illustrated by the following quotes:
Respondent 1: Tried this strategy and it works well.
Respondent 2: I work, thus learn best when I have a sense of
independence and freedom.
Respondent 3: This way you don't get left behind and you can
understand everything better, however you don't get challenged.
In summary, respondents expressed their overall positive attitude towards this
approach, which gave them “freedom” and allowed “better understanding of
everything.” However, the question about their awareness of and familiarity with
effective learning strategies needs to be asked. The findings indicated that respondents’
reflections about their learning skills were often based on a low awareness level of
Findings 205
effective learning strategies and confirmed the earlier-observed tendency of using
“trial and error” approach (sections 4.2.2 and 4.2.3).
The next section provides further data to support the finding that, at least some
respondents, were lacking effective learning strategies.
4.2.6 Self-modification of learning practices
This problem was further investigated by questions enquiring whether respondents
reflected on their learning experiences and whether they consciously modified their
learning practices to adjust them to the requirements of the blended learning approach
and learning networks.
In Unit S, 47% (25 of 53) of respondents and 57% (39 of 69) of respondents in
Unit E did not modify their learning practices. Two identified overarching categories
of reasons for not modifying their approaches were: 1) a lack of need; and 2) a lack of
motivation. The frequent statements classified in the first category were: “Don't feel
the real need to change at the moment”, or “Because I had no issues to begin with to
modify my study plan”. The second category contained fewer statements that pointed
towards the low development of self-regulated learning as the source of a lack of
motivation. For instance, one respondent commented: “I always planned to start
studying hard, but always gave up”, while another remarked: “If I implemented how I
wanted to learn, it would work, but I don't have time”. It appears that the respondents
were missing both the knowledge and the skills to apply effective learning strategies
to improve the effectiveness of their learning practices.
As for respondents who did modify their practices, 43% (30 of 69) in Unit E
and 53% (28 of 53) in Unit S implemented modifications in their learning practices.
The modified practices were classified in three overarching categories, and are
presented here in descending order of frequency of responses: 1) improved learning
strategies; 2) improved data handling/ management; and 3) increased study time.
Improved learning strategies related very often to changes made to consciously
engage with the knowledge, for instance by producing handwritten notes. The
following three responses illustrate the observed modifications:
Respondent 1: Amount of notes taken in lectures, shorten
handwriting.
Respondent 2: Neatness of notes.
206 Findings
Respondent 3: Notes by hand, not laptop - zone out otherwise.
Other examples of modifications classified in the first category included making
conscious decisions regarding learning practices. For instance, one respondent wrote:
“I started doing more at home vs at uni”, while another stated: “Less time on textbook
more on online resources”. The second overarching category (improved data handling/
management) often included statements such as “[Modified] method of material
storage”, “Hard copy of all computer labs in a single folder for reference”, or “Write
out code and keep in one place”. These examples indicate that modifications were
made after reflection, with the intention to improve effectiveness of learning. Finally,
for the third overarching category (increased study time) respondents would state:
“Increased time for study”.
Thus, the responses classified in the first two categories indicate that some
respondents reflected on the effectiveness of their learning strategies and made
conscious modifications with the purpose of improving their learning. Nevertheless,
the question about the effectiveness of the modifications remains. For instance, section
4.2.2 presented findings that many respondents were confused about the amount of
time they should or needed to spend on studying. Therefore, the doubt about
respondents’ level of awareness of effective learning strategies and their conscious
application remains. The data did indicate that respondents were modifying their
learning practices, after reflection; however, the possibility that the modifications were
based on “trial and error” remained (see sections 4.2.2 and 4.2.3).
4.2.7 Self-assessment of respondents’ learning skills
The questions discussed in this section investigated respondents’ self-assessment of
their learning skills. In Unit E, 100% (75) of respondents provided answers. Of those,
68% (51 respondents) believed they had appropriate learning skills to succeed in the
unit, 8% (6 respondents) did not believe they had the appropriate learning skills, and
24% (18 respondents) were not sure. In Unit S, 97% (58 of 60) of respondents provided
answers, including 57% (33) who felt they had appropriate learning skills to succeed
in the unit, 5% (3) who did not, and 38% (22) who were not sure.
The vast majority of respondents in both units, perceived themselves as being
well equipped with effective learning strategies to support their learning, enabling
them to complete their respective units successfully. Only a small number of
Findings 207
respondents were of the opposite opinion. This raises the question of whether
respondents were equipped with appropriate skills to reflect critically on their learning
practices, to assess their effectiveness, and to strategically select a more effective
learning strategy.
Data analysis of the final question confirms the above-formulated finding.
When asked what specific learning skills they would like to develop to improve
learning in their respective units, respondents predominantly pointed towards 1)
improved mathematical skills; 2) better time-management; and 3) lack of motivation
for studying. For instance, commenting on the issue of preparedness level in
mathematics, one respondent stated: “Maths and problem solving skills, IT skills”.
However, the most frequently cited learning skill was time-management. The
following three statements are representative for this overarching category:
Respondent 1: Not, busy, lazy & apathetic, how to manage time
effectively.
Respondent 2: Time-management and how not to procrastinate.
Respondent 3: Time management/ organisation for study.
It appears that many respondents were aware of their needs to improve the
effectiveness of their learning, but did not have the relevant level of knowledge to
correctly name the identified issue. Instead, they focused on time-management as the
main problem to address. This finding needs to be interpreted in light of findings from
previous sections, such as identified preference to learn online alone using assistance
from peers (section 4.1.2.2), low level of awareness of learning strategies, (sections
4.2.1 – 4. 2.6), including problematic strategies of catching up with missed timetabled
activities (section 4.2.4), inconsistent statements regarding study time (section 4.2.2),
and finally, a “trial and error” approach (sections 4.2.2, 4.2.3 and 4.2.6). Considering
all the above, respondents seemed to lack the self-regulatory skills that would enable
them to become active learners.
The next section looks at the respondents’ reactions to the suggestion of
explicitly teaching effective learning strategies.
208 Findings
4.2.8 Explicitly teaching effective learning strategies
Respondents’ reaction to the suggestion of teaching effective learning strategies was
sought during the focus groups. Overall, respondents saw benefit in improved learning
skills. One respondent described their positive reaction in the following way:
I think your suggestion would be very good because a lot of Unit
Coordinators cannot just stitch the subject together and then hope
you understand as much as they understand which doesn’t really
work. (Respondent 2, Focus Group 6)
Interestingly, the respondent attributed the need for learning about the
learning strategies to academics’ teaching skills rather than their own learning
skills. This, again, points towards a low awareness of respondents’ own
learning skills and the challenges of helping students realise the need to take
up the opportunity to learn how to learn. The following two quotations
illustrate the finding:
I found that when people tried to do that, they made me confused
because it’s like, … this is how they are telling you to learn, this is
how you need to learn and it doesn’t help. (Respondent 1, Focus
group 5)
I think it’s like a trial and error kind of thing. You need to define
what is going to work the best by yourself. (Respondent 2, Focus
Group 5)
Without a realised need for learning how to learn, improvement of one’s learning skills
and development of new learning strategies will not take place, and the perpetual
problem of constantly using “trial and error” approaches to learning may persist.
Consequently, this may prevent students from becoming self-regulated and self-
directed, active learners.
Findings 209
Section 4.2 has discussed findings in response to Research Question 2. The
next section shifts its focus from students to academic teaching staff and provides
answers to Research Question 3.
4.3 RESEARCH QUESTION 3: WHAT ARE ACADEMIC STAFF PARTICIPANTS’ PERCEPTIONS OF STUDENTS’ UPTAKE OF LEARNING AFFORDANCES AND THEIR NETWORKED LEARNING PRACTICES WITHIN PRESUPPOSED LEARNING NETWORKS?
The data analysis of the first two research questions made the importance of educators’
awareness of both, students’ learning networks, either formal or informal, and their
learning practices within identified networks, clear. The aim of the last research
question was to investigate academics’ perceptions of students’ uptake of learning
affordances and their learning practices. This question aimed to address the following
questions: Were academics’ perceptions of students’ uptake of affordances offered by
learning networks compatible with respondents’ self-reported uptake? Were academic
teaching staff’s perceptions of student learning practices compatible with respondents’
self-reported learning practices? What was the relationship between academics’
frames of reference, units’ functional context, academics’ perceptions and learning and
teaching practices? Finally, how did the findings of this research question impact on
learning design?
It is also important to note that, as the responses were provided by academic
teaching staff members, complex different perceptions were evident. The perceptions
resulting from academic teaching staff’s frames of reference coincided with their
perceptions of students’ practices and with students’ perceptions of learning
affordances provided by the functional context, which was largely designed by
academic teaching teams. As perceptions occurred simultaneously, some of them
converged, some diverged, which showed a third, combined perception. To name this
phenomenon, this study proposes the term interference of perceptions. The concept of
interference was borrowed from the physics discipline where it describes a
superposition of different waves of different amplitudes, which results in the creation
of another wave, and this has been adapted in this study to describe a constant interplay
of superposing perceptions. Different academics’ perceptions, based on different
foundations and experienced with different intensity levels (as demonstrated by the
data analysis), resulted in a unique perception of each, individual academic.
210 Findings
Furthermore, each unique perception was then superposed when designing and
teaching the selected units. This difficulty, often not (fully) realised by academic
teaching staff, may result in assumptions rather than evidence-based facts providing
the basis for decisions made. From a learning design perspective, the findings
emphasised the importance of knowing the students, having access to key and
comprehensive information about students’ frames of reference, and knowing which
aspects to consider prior to designing learning experiences for students.
4.3.1 Identified academic staff participants’ frames of reference
In addition to the demographic information in section 3.2.3, the eight academic staff
members from both units who participated in the study had varying degrees of teaching
experience ranging from 1 year (tutoring only) to 14 years of teaching, lecturing and
tutoring. On average however, the respondents had 3-4 years’ experience,
predominantly in tutoring, and they were confident they had appropriate teaching skills
to teach their respective units. Content analysis of the open-ended comments revealed
two overarching categories of respondents’ opinions: 1) confidence in teaching
abilities based on experience in tutoring; and 2) good design of the unit facilitating
teaching. For instance, one respondent stated: “I’m confident in my abilities as a
tutor/demonstrator because I adapt to my classes and their needs as the semester
progresses and I learn the personalities of each class, and each student”, while another
respondent wrote: “I have a lot of experience tutoring first year students in quantitative
methods”. In relation to the second identified overarching category, the following
answer illustrates the typical responses: “This unit is also very well designed to allow
me to facilitate learning rather than “teach” concepts. […] and this unit is well
structured”. Interestingly, respondents did not express the need for upskilling of their
teaching skills and did not suggest any additional teaching skills they would like to
develop to improve their teaching methods.
Data analysis further revealed that academics who designed the units were
either not involved in teaching (only coordinating), or were lecturing and coordinating
the units, but not tutoring. Such a division of roles places the responsibility with the
unit coordinators to effectively communicate the teaching philosophy underpinning
the learning design, and the preferred pedagogical approaches, to their teams. The
problem arises when the unit coordinators are not involved in the learning design
Findings 211
process, and are thus not fully prepared to communicate the learning design principles
to their teaching teams. This problem is further discussed in section 4.3.4.
4.3.2 Identified academic staff perceptions of the students’ uptake of learning
affordances offered by learning networks
Questions in this part of the questionnaire mirrored the student version and enquired
about staff perceptions of student uptake of affordances offered by the following
elements of learning networks: 1) ideas (i.e. lectures, workshops and computer labs);
2) people; and 3) tools. There were no questions about artifacts made by students, as
this could not be observed by the teaching staff.
4.3.2.1 Response rate
Response rates across all questions, in both units, varied significantly. In addition, the
number of respondents was low. For instance, to questions investigating teaching
staff’s perceptions of students’ engagement with lectures, three respondents provided
answers: a unit coordinator and a lecturer, both involved in lecturing in the units, and
one tutor from Unit E. By contrast, questions investigating the same problem in the
context of workshops were answered by seven out of eight surveyed staff, with only
one academic staff member not providing answers. Finally, questions looking at
respondents’ perceptions of students’ engagement with computer laboratories were
answered by three respondents (out of five), as this timetabled activity was offered
only in Unit S.
4.3.2.2 Ideas
The concept of learning and teaching approaches, and elements of learning networks,
were explained in sections 1.4 and 2.2.2, and their analysis from a student participant
perspective was presented in section 4.1.2. In short, this study has used the concept of
ideas as the learning and teaching approach used during timetabled activities, and
elements of formal learning networks applied in the two units under investigation for
face-to-face, on-campus learning. This included lectorials and workshops in Unit E,
and lectorials, workshops and computer laboratories in Unit S.
212 Findings
4.3.2.2.1 Typology of learning affordances offered by timetabling activities,
intended interactions, and staff perceptions of their uptake by students
The study investigated academic respondents’ perceptions of students’ uptake of
learning affordances offered by timetabled activities, mapped against associated
intended interactions. Table 4.18 presents staff perceptions of students’ uptake of
learning affordances offered by timetabled activities, mapped against intended
interaction and organised in descending order of perceived importance.
As expected, the first affordance perceived by the academic teaching staff
across all timetabled activities was the opportunity to learn and practice new content.
Next, a closer look at the data revealed that the only one affordance for which the
importance was consistently perceived differently across all three timetabled activities
was the opportunity for learning about/ practising for assessment. The reason for this
discrepancy may lay with the staff’s frames of reference. The academic teaching staff
were less interested in practising for assessment than in teaching content knowledge.
In addition, the functional context of individual timetabled activities also impacted on
the ways academic teaching staff were perceiving the assessment. As the teaching
objective of the workshops was to enable authentic and active learning, this could
influence the academic staff’s perception of the importance of practising for
assessment, which may be offered by this type of timetabled activity.
The difference in perceiving the remaining learning affordances by academic
teaching staff lay between lectures and the other two timetabled activities. Learning
directly from/with the experts (i.e., the academic teaching staff) was perceived as an
important affordance, ranked second for lectures and third for computer laboratories.
As for workshops, this affordance was perceived as less important than working
collaboratively with other students. This finding suggests that the academic teaching
staff perceived opportunities for learning from/with experts in relation to the type of
the functional context (i.e., functional contexts of lectures and computer laboratories
enable learning from/ with experts, workshops enable learning collaboratively with
peers).
Find
ings
213
Tabl
e 4.
18:I
mpo
rtanc
e fo
r stu
dent
s of a
ffor
danc
es o
ffer
ed b
y tim
etab
led
activ
ities
, map
ped
agai
nst i
nten
ded
inte
ract
ions
, as p
erce
ived
by
acad
emic
teac
hing
staf
f.
All
colo
urs f
ollo
w th
e sa
me
conv
entio
n as
for s
tude
nts’
dat
a.
214 Findings
Interestingly, socialising with experts during lectures was perceived by
academics as an important affordance for students and was ranked third. Furthermore,
academics also considered opportunities for socialising with experts during lecture
time to be more important than during workshops or computer laboratories. This
finding conflicts with the findings in section 4.1.2, which indicated student
respondents’ preference to learn from experts during lecture time, rather than socialise
with them.
Conversely, academic teaching staff’s opinions that students attended lectures
predominantly to learn from an expert confirmed the findings in section 4.1.2. That is,
sections 4.2.1 and 4.2.3 demonstrated that during lectures, students were expecting
knowledge transmission from the lecturers, which may explain the low uptake of
opportunities for interaction during lectures. Data analysis of self-reported academic
staff’s perceptions revealed a similar finding. It appears that opportunities for
interacting with experts and peers during lectures were not perceived as important by
academics, and were therefore ranked at the bottom.
As noted above, academic teaching staff perceptions of learning affordances
offered to students by workshops and computer laboratories were almost uniform. The
only difference related to the nature of learning with/ from experts/ peers. In contrast
to the findings in the student version of the questionnaire, the results indicated that
from academics’ perspective the workshops offered, by default, opportunities for
learning with peers in the first instance, and secondly with experts.
Tables 4.19, 4.20 and 4.21 present a comparative analysis of students and
academic teaching staff’s responses that confirm above-discussed findings.
Find
ings
215
Tabl
e 4.
19:C
ompa
rativ
e an
alys
is o
f upt
ake
of le
arni
ng a
ffor
danc
es p
rovi
ded
by le
ctur
es –
staf
f and
stud
ents
.
TIM
ET
AB
LED
AC
TIV
ITY
(L
ectu
res)
STU
DEN
TS
STA
FFIM
POR
TAN
CE
IMPO
RTA
NC
E
Ran
king
Aff
orda
nces
Gro
upn
Med
ian
(8-1
)
Inte
rqua
rtile
ra
nge
25-7
5%R
anki
ngA
ffor
danc
esn
Med
ian (8-1
)
Inte
rqua
rtile
ran
ge25
-75%
1Pr
ovid
e ne
w c
onte
nt
Uni
t E75
87-
81
Prov
ide
new
con
tent
4
88-
8U
nit S
607
6-8
2Le
arn
dire
ctly
from
the
lect
urer
Uni
t E74
6.5
5-8
2Le
arn
dire
ctly
from
the
lect
urer
46
5-6
Uni
t S60
75-
7
3Le
arn
abou
t ass
essm
ent i
n th
is
unit
Uni
t E75
65-
63
Mee
t fac
e-to
-fac
ew
ithth
e le
ctur
er4
64.
5-7
Uni
t S60
52-
6
4A
sk q
uest
ions
/ see
k cl
arifi
catio
ns d
irect
ly fr
omth
e le
ctur
er
Uni
t E74
43-
64
Lear
n ab
out a
sses
smen
t in
this
un
it4
53-
6.5
Uni
t S60
53-
6.5
5M
eet f
ace-
to-f
ace
with
the
lect
urer
Uni
t E74
42-
55
Mee
t fac
e-to
-fac
ew
ith o
ther
st
uden
ts4
54-
5U
nit S
604.
53-
6
6
Mee
t fac
e-to
-fac
ew
ith o
ther
st
uden
tsU
nit E
744
3-5
6
Ask
que
stio
ns/ s
eek
clar
ifica
tions
dire
ctly
from
the
lect
urer
44
3-5
Uni
t S60
32-
5
7
Ask
que
stio
ns/ s
eek
clar
ifica
tions
dire
ctly
from
othe
r st
uden
ts
Uni
t E74
43-
6
7
Ask
que
stio
ns/ s
eek
clar
ifica
tions
dire
ctly
from
othe
r stu
dent
s4
42-
6U
nit S
603
2-5
8Le
arn
dire
ctly
from
othe
r st
uden
ts
Uni
t E74
32-
58
Lear
n di
rect
ly fr
omot
her
stud
ents
4
11-
1U
nit S
603
2-4
216
Find
ings
Tabl
e 4.
20:C
ompa
rativ
e an
alys
is o
f upt
ake
of le
arni
ng a
ffor
danc
es p
rovi
ded
by w
orks
hops
–st
aff a
nd st
uden
ts.
Tabl
e.4.
21:C
ompa
rativ
e an
alys
is o
f upt
ake
of le
arni
ng a
ffor
danc
es p
rovi
ded
by c
ompu
ter l
abor
ator
ies –
staf
f and
stud
ents
.
STU
DEN
TS
STA
FFT
IME
TA
BL
ED A
CT
IVIT
Y
(Wor
ksho
ps)
IMPO
RTA
NC
EIM
POR
TAN
CE
Ran
king
Aff
orda
nces
Gro
upn
Med
ian
(7-1
)
Inte
rqua
rtile
ra
nge
25-7
5%R
anki
ngA
ffor
danc
esn
Med
ian
(7-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
1Pr
actis
e th
e ne
w c
onte
nt
pres
ente
d at
the
lect
ure
Uni
t E75
76-
71
Prac
tise
the
new
con
tent
pr
esen
ted
at th
e le
ctur
e 8
11-
1.75
Uni
t S60
75.
75-7
2W
ork
colla
bora
tivel
y w
ith h
elp
from
teac
hers
, if n
eede
dU
nit E
755
4-6
2Pr
actis
e fo
r ass
essm
ent
83
2-3.
75U
nit S
605
4-6
3W
ork
colla
bora
tivel
y w
ith o
ther
st
uden
tsU
nit E
754.
53-
63
Wor
k co
llabo
rativ
ely
with
ot
her s
tude
nts
83.
52-
4.75
Uni
t S60
53.
75-6
4Pr
actis
e fo
r ass
essm
ent
Uni
t E75
54-
64
Wor
k co
llabo
rativ
ely
with
he
lp fr
omte
ache
rs, i
f nee
ded
84
3-4.
75U
nit S
604
2-6
5M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
Uni
t E75
32-
45
Mee
t fac
e-to
-fac
e w
ith o
ther
st
uden
ts8
42.
25-5
Uni
t S60
34-
26
Mee
t fac
e-to
-fac
e w
ith th
e te
ache
rsU
nit E
753
2-5
6M
eet f
ace-
to-f
ace
with
the
teac
hers
84.
51.
75-6
Uni
t S60
32-
57
Prac
tice
lear
ning
skill
s (w
ork
orga
nisa
tion,
tim
e-m
anag
emen
t, fo
cus,
etc.
)
Uni
t E75
11-
3.75
7Pr
actic
e le
arni
ng sk
ills (
wor
kor
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
87
1.25
-7
Find
ings
217
STU
DEN
TS
STA
FF
Ran
king
Aff
orda
nces
nM
edia
n (8
-1)
Inte
rqua
rtile
ra
nge
25-7
5%R
anki
ngA
ffor
danc
esn
Med
ian
(8-1
)
Inte
rqua
rtile
ra
nge
25-7
5%
1Pr
actis
e th
e ne
w c
onte
nt
pres
ente
d at
the
lect
ure
(and
the
wor
ksho
p)
498
6-8
1Pr
actis
e th
e ne
w c
onte
nt
pres
ente
d at
the
lect
ure
(and
the
wor
ksho
p)
31
1-1
2W
ork
indi
vidu
ally
, with
hel
p fr
omth
e te
ache
rs, i
f nee
ded
497
5-7
2A
cces
s spe
cial
ised
softw
are
32
2-5
3Pr
actic
e fo
r ass
essm
ent
496
3-7
3M
eet f
ace-
to-f
ace
with
the
teac
hers
33
3-6
4W
ork
indi
vidu
ally
with
hel
p fr
omot
her s
tude
nts,
if ne
eded
405
3-6
4W
ork
indi
vidu
ally
, with
hel
p fr
omth
e te
ache
rs, i
f nee
ded
34
2-7
5M
eet f
ace-
to-f
ace
with
the
teac
hers
394
3-5
5W
ork
indi
vidu
ally
with
hel
p fr
omot
her s
tude
nts,
if ne
eded
35
4-5
6A
cces
s spe
cial
ised
softw
are
414
3-6
6M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
35
4-6
7M
eet f
ace-
to-f
ace
with
oth
er
stud
ents
353
2-4
7Pr
actic
e fo
r ass
essm
ent
36
3-7
8Pr
actic
e le
arni
ng sk
ills (
wor
kor
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
362
1-5
8Pr
actic
e le
arni
ng sk
ills (
wor
kor
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
37
0-8
All
colo
urs f
ollo
w th
e sa
me
conv
entio
n as
for s
tude
nts’
dat
a.
218 Findings
In addition, this also revealed one difference in the perceived affordances
offered by the computer laboratories. Academics highly valued the opportunity for
accessing specialised software while in the student respondents’ version this
affordance was ranked in third to last position (out of eight), which can be explained
by the differences in respondents’ frames of reference. It seems that academic teaching
staff understood the value of specialised software and the benefits of accessing it on-
campus, during face-to-face activities, when assistance from experts or peers was
available. It also appears that students focused primarily on the opportunity to practise
and learn from experts and with peers. Further research is needed to investigate the
reasons why students did not perceive the opportunities afforded by the use of
specialised software.
Interestingly, the opportunity to practise learning skills was consistently
perceived as the least important by both cohorts. It appears that academic teaching
staff did not perceive the opportunities for fostering students’ self-regulation by
assisting students to effectively use their learning networks. This has consequences for
learners’ practices, as it was difficult for staff to attract students’ attention to the
opportunities for learning if they did not even perceive such opportunities in the first
instance. Section 4.3.4 presents findings from focus groups and discusses this issue in
more detail.
This section has discussed the perception and uptake of affordances offered by
(the concept of) ideas, and briefly analysed the results in relation to the students’
version of the questionnaire. The next section investigates academics’ perceptions of
affordances, and their uptake, offered by people, the second element of learning
networks.
4.3.2.3 People
This part of the questionnaire explored the academic staff’s perceptions of the way
students were taking up affordances for learning offered by social agents, constituents
of formal and informal learning networks.
4.3.2.3.1 Typology of learning affordances offered by people, intended
interactions, and staff perceptions of their uptake by students
Findings 219
Based on the data analysis of academic teaching staff’s responses a certain pattern in
staff’s perceptions of students’ practices could be observed. Interestingly, the observed
pattern showed similarities to students’ responses discussed in Section 4.1.2.2.
Table 4.22 presents a comparative analysis of results from academic teaching
staff and students’ questionnaires. Responses in both cohorts pointed towards learning
within formal learning networks as the most important learning affordance. The
(small) difference between the responses from the two cohorts lies in the type of social
agent to learn from/ with. Academic staff ranked learning from university teaching
staff as the most important affordance, while learning with peers was perceived as the
second most important. Students’ responses in both units showed the opposite. This is
consistent with findings in section 4.1.2.2 which related to patterns of contacting social
agents for learning/ assistance by students.
The second position in academics’ responses was taken up by fellow students,
close peers, and/or classmates, and no differentiation was made between the two types
of peers. Finally, academics ranked university support staff in third position, or last in
terms of formal learning networks.
In informal learning networks, the order of social agents mentioned varied. For
instance, family members were not perceived by academic teaching staff as important
social agents assisting learners with their studies. This affordance was ranked seventh,
or second to last, while in students’ responses, family members were on the top of their
list of social agents and experts to whom the students would turn for assistance.
Instead, academics perceived experts belonging to informal learning networks (i.e.,
private tutors and teaching staff from other universities) as important social agents,
which was not the case for student respondents, even though there were some
differences between the two student cohorts. This might suggest that, from the
academics’ perspective, teaching staff from other universities and private tutors had
the same status as academic experts themselves, which is not always the case. This
observation was confirmed by academics’ perceptions of other students and family
members. They were not perceived as important (occupying sixth and seventh
positions respectively), which suggests that they were not part of academics’ frames
of reference (i.e., to be an academic teacher), and thus were not directly associated
with formal learning.
220
Find
ings
Tabl
e 4.
22:C
ompa
rativ
e an
alys
is o
f upt
ake
of le
arni
ng a
ffor
danc
es p
rovi
ded
by so
cial
age
nts –
staf
f and
stud
ents
.
PEO
PLE
(Soc
ial
Age
nts)
STU
DEN
TS
STA
FF
Ran
king
Typ
e of
soci
al a
gent
Gro
upn
Med
ian
(8-1
)
Typ
e of
th
e ne
twor
k
Inte
nded
in
tera
ctio
nR
anki
ngT
ype
of so
cial
ag
ent
nM
edia
n(8
-1)
Inte
rqua
rtile
ra
nge
25-7
5%
Typ
e of
the
netw
ork
Inte
nded
in
tera
ctio
n
1Fe
llow
stud
ents
en
rolle
d in
this
unit
Uni
t E64
8Fo
rmal
Lear
ning
fr
om/w
ithpe
ers
1Q
UT
teac
hing
st
aff
88
Form
alLe
arni
ng
from
/with
ex
perts
Uni
t S46
88-
8
2Q
UT
teac
hing
staf
fU
nit E
377
Form
alLe
arni
ng
from
/with
ex
perts
2Fe
llow
stud
ents
en
rolle
d in
this
unit
87
5-7
Form
alLe
arni
ng
from
/with
peer
sU
nit S
337
3
QU
T su
ppor
t sta
ff
(e.g
. STI
Mul
ate,
Li
brar
y)
Uni
t E35
7Fo
rmal
Lear
ning
fr
om/w
ith
expe
rts3
QU
T su
ppor
t st
aff (
e.g.
ST
IMul
ate,
Li
brar
y)
8
65-
6
Form
alLe
arni
ng
from
/with
ex
perts
Uni
t S35
7
4Fa
mily
mem
ber
Uni
t E37
5In
form
alLe
arni
ng
from
/with
ex
perts
4Pr
ivat
e tu
tors
8
54-
5In
form
alLe
arni
ng
from
/with
ex
perts
Uni
t S28
5.5
5St
uden
tsfr
om o
ther
U
nive
rsiti
es
U
nit E
305
Info
rmal
Lear
ning
fr
om/w
ith
peer
s5
Teac
hing
staf
ffr
om o
ther
U
nive
rsiti
es
8
42.
5-5
Info
rmal
Lear
ning
fr
om/w
ith
expe
rtsU
nit S
224.
5
6Te
achi
ng st
afff
rom
ot
her U
nive
rsiti
es
U
nit E
233
Info
rmal
Lear
ning
fr
om/w
ith
expe
rts6
Stud
ents
from
ot
her
Uni
vers
ities
84
3-4
Info
rmal
Lear
ning
fr
om/w
ith
peer
sU
nit S
193
7Pr
ofes
sion
als
Uni
t E21
3In
form
alLe
arni
ng
from
/with
ex
perts
7Fa
mily
mem
ber
83
2-3
Info
rmal
Lear
ning
fr
om/w
ith
expe
rtsU
nit S
203
8Pr
ivat
e tu
tors
U
nit E
232
Info
rmal
Lear
ning
fr
om/w
ith
expe
rts8
Prof
essi
onal
s8
1.5
1-2
Info
rmal
Lear
ning
fr
om/w
ith
expe
rtsU
nit S
242.
5
Findings 221
Finally, the affordance of learning from professionals, presenting an
opportunity for developing one’s self-direction and promoting epistemic fluency, was
ranked consistently, by all respondents in both cohorts, at the bottom (last or second
last) of the questionnaire results. This suggests that professionals were not perceived
by either students or academic teaching teams as part of the academic learning. These
observations are further discussed in Chapter 5.
In summary, the comparative analysis of data led to a pattern that academic
teaching staff applied to their ways of perceiving students’ practices. Some of the
elements of the pattern were similar to students’ perceptions, while some were
different. For instance, academics thought about constituents of formal learning
networks as the first social agents to be contacted. As this finding is consistent with
findings from student questionnaires, it can be concluded that this is a common pattern
of practice: within educational settings, members of formal learning networks will be
contacted first.
Next, it appears that within formal learning networks, academics also made a
distinction based on the type of ecological system social agents belonged to; however,
the pattern was nuanced in this respect. That is, social agents who were considered
members of academics’ microsystems (university teaching staff) were thought to be
contacted first by students, followed by members of academics’ exosystems (students’
peers and/or classmates belonging to students’ micro, or mesosystems), and finally
members of academics’ mesosystems (university support staff).
As for learning within informal learning networks, the constructed pattern
seemed to depend on the attribute of expertise, which is associated with ecological
systems. In short, those members who were perceived as experts and as belonging to
academics’ mesosystems (teaching staff from other university, private tutors) were
ranked higher than experts belonging to exosystems (professionals). As this is
consistent with findings from the student version of the questionnaire (section 4.1.2.2),
it can be concluded that the two factors (perceived status of an expert/ peer, and
belonging to one of the ecological systems) strongly influenced students’ and
academics’ perceptions and uptake of learning affordances offered by social agents.
These findings exemplify the complexity of perceptions and the impact of differences
in viewpoints, which are further discussed in section 5.4.1.
222 Findings
4.3.2.4 Tools
The academic teaching staff version of the questionnaire looked at their perceptions of
the way students were taking up affordances for learning offered by electronic devices
and online learning tools, all elements of formal and informal learning networks.
However, as it was too difficult for academic teaching staff to make observations about
the ways textbooks and eBooks were used by students, these tools were not
investigated.
4.3.2.4.1 Electronic devices
Analysis of the questionnaire data revealed discrepancies between the ways academic
teaching staff perceived the importance of electronic devices used by their students,
and students’ actual responses. Table 4.23 provides a comparative summary of the
findings.
According to academic staff, students were using predominantly laptops and
tablets for their learning. Desktop computers and mobile phones were perceived as less
important. While the perceived importance of laptops and desktop computers to some
extent reflected students’ responses (ranked at first and third positions in both student
and academic staff questionnaires), the perceived importance of tablets and mobile
phones differed. Students from both units attributed high importance to their mobile
phones (ranked second) and low importance to their tablets (ranked fourth). This is in
complete contrast to academic teaching teams’ responses who perceived tablets as
more important for students than mobile phones. These discrepancies in perceptions
suggest that academic staff might have based their opinions on personal assumptions,
resulting from their own frames of reference (personal experiences) rather than from
evidence.
Find
ings
223
Tabl
e 4.
23:C
ompa
rativ
e an
alys
is o
f upt
ake
of le
arni
ng a
ffor
danc
es p
rovi
ded
by e
lect
roni
c de
vice
s –st
aff a
nd st
uden
ts
DE
VIC
ES
STU
DEN
TST
AFF
Ran
king
Typ
e of
the
devi
ceG
roup
nM
edia
n(4
-1)
Inte
rqua
rtile
ra
nge
25-7
5%
P*s
igni
fican
t at
5%
leve
lR
anki
ngT
ype
of th
e de
vice
nM
edia
n(4
-1)
Inte
rqua
rtile
ra
nge
25-7
5%
1Y
our l
apto
pU
nit E
694
4-4
0.93
61
Lapt
op8
43-
4U
nit S
494
4-4
2Y
our m
obile
pho
ne
Uni
t E69
32-
30.
464
2Ta
blet
8
2.5
2-4
Uni
t S38
32-
3
3Y
our d
eskt
op
com
pute
r U
nit E
443
2-4
0.92
23
Des
ktop
com
pute
r 8
22-
2U
nit S
303
2-4
4Y
our t
able
t U
nit E
262
1-3
0.16
24
Mob
ile p
hone
8
1.5
1-3
Uni
t S23
11-
3
224 Findings
This suggestion is reinforced by analysis of open-ended comments in academic
teaching staff’s questionnaire. In the accompanying comments respondents observed
that, overall, students were making good use of their electronic devices. One
respondent, however, critically reflected on the negative impact that the growing
number of learning resources and their increased variety were having on students. In
the respondent’s opinion, this was causing difficulties for students in self-regulating
their learning:
I think they face great challenges. They are being given too many
learning resources on too many different devices. This spreads the
information rather than structure it into a single place. I can only
imagine this information is likewise spread, diffuse, and therefore
not as sharp, in their brain.
This reflection indicates the respondent’s awareness of the difficulties faced by
learners in terms of the ways learning resources are presented, and the lack of assistance
in using them (including learning strategies) provided to students. It is important to
note that this respondent was not a unit coordinator, and hence was not initially
involved in designing students’ learning experiences. Therefore, the respondent had no
previous knowledge of students’ practices in using electronic devices or, more broadly,
online learning resources, based his/her opinion on assumption rather than facts.
This observation also leads to the next section that discusses academic teaching
staff’s perceptions of the uptake of learning affordances offered by online learning
tools and their importance to students.
4.3.2.4.2 Typology of learning affordances offered by tools, intended
interactions, and staff perceptions of their uptake by students
Analysis of responses about the perception and uptake of online learning tools allowed
for a comparison between academic teaching staff’s and students’ responses, which is
presented in Table 4.24.
Overall, the findings are very similar to the findings from the student version of
the questionnaire, which were presented in section 4.1.2.3. With the exception of
general websites, all responses from both students and academic teaching staff
indicated a similar pattern of using the tools. First, elements of formal learning
Findings 225
networks were perceived to be used, followed by the elements classified as somewhere
between formal or informal learning networks (depending on the functional context of
the units), and finally elements of informal learning networks.
It appears that, from the perspective of academic teaching staff, their students
preferred to interact with online tools in formal learning networks, which are part of
students’ immediate surrounding microsystem, first (i.e., LMS site and the unit’s
Facebook page), and which require limited levels of autonomy. Next, students were
perceived to interact with elements in their mesosystems (e.g., library site, YouTube,
Khan Academy) that belonged to either formal or informal learning networks,
depending on the units’ functional context, and that required limited to medium levels
of autonomy. Finally, in academics’ perceptions, students were using elements of
informal learning networks (e.g., social media, mobile phone apps), and parts of their
exosystems least frequently, as these were perceived (by academics) as the least
important tools.
Interestingly, as an exception to the observed pattern, internet websites (general)
were observed in both students’ and academic teaching staff’s responses. Similarly to
the observations made in section 4.1.2.3, it appears that respondents’ frames of
reference (i.e., seeing the use of the internet as part of learning and, more broadly, of
digital literacy) and the functional context of the units (i.e., requirement of conducting
independent research/ autonomous learning online) impacted on their perceptions.
While commenting on students’ use of online tools, most academic teaching staff
agreed that students were making good use of online tools; however, they also added
that students were highly pragmatic in their practices, using online tools such as
websites to quickly find answers or to get “last-minute” assistance with assessments.
One respondent wrote: “Students are good in typing a question into Google”, while
another commented: “When the students want something, they’ll look everywhere for
help that will meet them on their terms. That is, the day its due when they’re at home
in their pyjama pants”.
No critical reflection was evident around the possibility of task and assessment
design enabling and/ or encouraging these types of student practices.
226
Find
ings
Tabl
e 4.
24:C
ompa
rativ
e an
alys
is o
f upt
ake
of le
arni
ng a
ffor
danc
es p
rovi
ded
by o
nlin
e le
arni
ng to
ols –
staf
f and
stud
ents
.
Findings 227
Finally, there was a discrepancy between academic teaching staff’s and students’
perceptions of MOOCs and their uptake by students. Both student cohorts classified
this affordance as the least important, and the least frequently and intensely taken up.
This finding was presented in section 4.1.2.3 as resulting from MOOCs belonging to
respondents’ informal learning networks and requiring high levels of autonomy, as
well as being part of the respondents’ exosystem. On the other hand, academic
teaching staff perceived MOOCs as an online tool that is more important, and they
perceived it to be more frequently and intensely taken up by students than was
suggested by students’ responses. This disjunction of opinions could be seen as
resulting from interference of perceptions and may lead to various assumptions. That
is, academic teaching staff’s frames of reference of self-directed learners allowed them
to see the benefits of MOOCs, as part of their own informal learning networks, but
their use requires autonomy in learning. Yet what was obvious to independent learners
accustomed to challenges of autonomous learning, was not obvious to, and hence not
perceived by, students. It appears that academics did not realise this disjunction, which
could then lead to an assumption that students actually perceive MOOCs as more
important and that they take up affordances offered by this tool more frequently and
intensely than is actually the case.
The reliability of this finding is strengthened not only by the fact that responses
from the two cohorts showed the same finding (yellow in Table 4.24), but also by the
fact that one academic staff member expressed doubts about the suitability of
answering this part of the questionnaire. In open-ended comments, this respondent
stated: “This is what I assume but it is not for me to answer. It’s not what I think should
be”. Interestingly, this particular type of reflection was made here for the first time by
respondents, and it was related to academics’ limited possibilities of observing
students’ activities within an online environment.
4.3.3 Identified academic staff perceptions of students’ networked learning
practices
As it was beyond academic teaching staff’s capacity to observe students’ learning
practices outside their in-class, face-to-face environment, this part of the questionnaire
focused on investigating staff’s perceptions of students’ study practices. These
included whether, in academic teaching staff’s perceptions, students were studying for
228 Findings
timetabled activities and how much time per week they should spend on studying, their
attendance at timetabled activities, and their strategies for catching up (or not) with
missed content. Finally, this part also investigated academic staff’s perceptions of the
effectiveness of students’ learning practices.
4.3.3.1 Studying for timetabled activities
Data analysis of questionnaire answers related to the time students should
spend studying revealed a broad spectrum of academic teaching staff’s opinions,
ranging from 3-6 hours to 10 hours (and even 15 in one case) per week, as discussed
in section 4.3.1. Two types of discrepancies can be identified within these responses.
First, the broad spectrum of responses provided by academic teaching staff indicates
that the respondents were basing their opinions on personal impressions and/ or
approximations. The second discrepancy relates to the estimated time noted by
academics and students. It appears that not only did the student cohort lack awareness
of self-regulatory practices and strategies (see section 4.2.2), but academic teaching
staff were also confused about what constituted effective self-regulatory strategies,
including time-management.
The analysis of the open-ended comments confirmed this finding by providing
evidence of personal impressions and approximations used by academic teaching staff
as foundations for advice about study time. For instance, one respondent justified
recommending 10 hours of study per week by asserting: “It's a full-time study load”.
Another respondent suggested that students already knew the content and needed to
practice “for mastery” for approximately 1-2 hours. Yet another respondent suggested
1-3 hours “depending on how familiar they are with the content already. Some have
covered this content in high school and need less revision”. Many of these comments
were based on academic teaching staff’s perceptions and assumptions rather than on
evidence (e.g., in form of data on students’ previous studies).
Overall, the findings from both staff and students are similar and point towards
the importance of raising learners’ and academic teaching staff’s awareness of learning
strategies that enable application of self-regulatory learning practices.
This finding was confirmed by data about academic teaching staff’s perceptions of
students study practices for timetabled activities. Overall, in the respondents’ opinions,
students did not study for any of the three types of timetabled
Findings 229
activities: lectures, workshops or computer laboratories. This, to some extent, is
similar to the findings in section 4.2.2, which indicated that many students either did
not study for timetabled activities, or strategically studied for assessment only.
However, students’ responses also indicated that they would study at home, alone,
using assistance from members of their microsystems (e.g., close peers and/ or family
members, experts in the discipline), who are constituents of their informal learning
networks (section 4.1.2.2.2). This was not perceived by academics. For instance, the
in-depth analysis of open-ended comments revealed respondents’ doubts about
whether students were studying for lectures. One respondent noticed: “They like to
have some preparative notes available before the lecture. But it could also just be so
they don’t need to write down everything that the lecturer writes…”.
Interestingly, some academic teaching staff attempted to explain the reasons
for students not studying for timetabled activities: “They are first year students, often
with bad study habits”. Some respondents made observations about the impact of unit
design, especially the timing of individual learning activities on students’ learning
practices, as illustrated in the following:
If they attend the computer labs prior to the workshops (as they are
supposed to) then they are implicitly preparing for the workshop.
However, if they skip the lab, I do not think they are preparing for
the workshop.
It appears that the functional context did attract students’ (at least some of them)
attention towards the learning opportunity of studying for workshops. The question,
however, is whether the students attended the computer laboratories, which was one
of the conditions for studying for workshops. This finding also illustrates the
importance of perceiving learning flow and, when designing for a blended learning
mode, emphasising the importance of attendance at the timetabled activities (parts of
the learning flow).
4.3.4 Focus group findings
Participants’ demographics and teaching experiences were discussed in detail in
section 3.2.3.2. Still, these diverse backgrounds of participants might have impacted
on their perceptions, personal impressions and academic teachers’ beliefs. Data
230 Findings
analysis revealed categories of academics’ perceptions emerging from reflections
made by participating academics in both units. Interestingly, the identified categories
were identical in both units, and highly similar to findings identified through
questionnaire analysis. Such high similarity of findings resulting from analysis that
used different data analysis methods (section 3.3.5) strengthens the trustworthiness,
objectivity, reliability and validity of the findings.
The identified categories were classified in two overarching categories of
academics’ perceptions: 1) assumptions made by teaching academics; and 2) diverse
levels of familiarity with and/or awareness of pedagogical approaches used in the
context of the units under investigation.
4.3.4.1 Assumptions
The data analysis identified two types of assumptions made by academic teaching
staff. First, and expressed consciously, was the frequent use of the verb “I think”,
followed by expressions such as “like”, “it seems” or “perhaps” and verbs in
conditional mode, for example “ I would say” or “ I could say”. The following sentence
is a typical example of an assumption being made consciously by a respondent: “Like,
just from observation, I think, it seems like …” (Unit S, Respondent 1).
The second type, unconscious, resulted from interference of perceptions (see
sections 4.3.2 and 4.3.3). Analysis of focus group transcriptions identified four
categories of assumptions about students: 1) motivations to study their chosen
discipline; 2) preparedness level in mathematics; 3) preparedness to study at the
university; and 4) learning practices.
The following two quotations illustrate the identified assumptions regarding
students’ motivations to study their chosen discipline:
A lot of them either didn’t know what else to do or know that, given
the direction of everything (Unit S, Respondent 1).
Some of them just like it, but most want to get a good job afterwards.
(Unit E, Respondent 1)
This is representative of all academic teaching staff, from both units of study, as is the
next one, illustrating an academic’s perception of students’ preparedness level in
Findings 231
mathematics: “Maybe half of them do have the background knowledge, before coming
to this unit” (Unit S, Respondent 1).
Interestingly, academic teaching staff had similar opinions about students’
preparedness levels in mathematics from high school. The critical perceptions were
not limited to the content of mathematical knowledge, but also concerned the learning
skills students brought with them, thus leading to another set of assumptions about
students’ preparedness to study:
And any additional mathematics they have to learn they expect it to
be as easy as it has been in high school. The extension of high school
mathematics. (Unit S, Respondent 3)
I don’t know very well the high school system, but I think they are
not ready for studying at uni. The system doesn’t equip them with
skills, they do not take responsibility for their learning, they do not
know what to do when they are struggling. (Unit E, Respondent 2)
In this way academic teaching staff explicitly expressed their lack of knowledge of
and/ or familiarity with the high school system, including the mathematics curriculum.
And yet, strong opinions, based on perceptions, were presented.
Finally, the assumptions classified in the fourth category, students’ learning
practices, were in clear contrast to students’ responses about their learning practices
presented in section 4.2. Academic teaching staff assumed that students were working
predominantly on-campus or at home, at night:
They think that there is no lot of the outside work. They think that
they can do everything in class and then the outside the class is
their own time. (Unit S, Respondent 1)
A lot of the problems is that they do their work at night. Like, the
most part of the day they are busy doing other stuff, you know. So,
they realise late, at night that they need to catch up with the content.
In class or during the day, on campus, they don’t realise this.
Actually going and doing your own study happens at night, at home.
(Unit S, Respondent 1)
232 Findings
This suggests that the participants were basing their claims on general perceptions,
previous personal experiences or general impressions, rather than evidence.
Yet, when presenting an opinion or advancing an argument based on evidence,
academic teaching participants made this clear in their statements. For instance, when
talking about students’ preparedness levels in mathematics, to strengthen the presented
argument, one participant remarked: “That’s pulled out of the survey which I run in
this unit about students’ background and attitudes towards mathematics” (Unit S, UC).
In summary, participants were quite frequently making assumptions
unsupported by evidence, based on their personal impressions and experiences.
The next section discusses the second overarching concept, which is related to
participants’ diverse levels of familiarity with, and understanding of, approaches to
learning and teaching applied within the context of the units in focus.
4.3.4.2 Diverse levels of familiarity with and/or awareness of pedagogical approaches of units used in the context of the study
Data analysis revealed some interesting patterns of academic teachers’ awareness of
principles of learning and familiarity with selected pedagogical approaches.
Overall, it appears that the participants had a good understanding of the
importance of active approaches to learning. As one participant stated: “But I think
learning is an active thing, it’s not something that you can just scroll through the PDF
and absorb the information by osmosis” (Unit S, UC). In addition, examples of good
teaching practice were also identified. For instance, the practice of addressing students
(hence, interacting with them) was mentioned by some participants. One participant
noted:
I also think one thing the unit coordinator should do is to ask
questions directly to students. If you ask a general question, some
students would never answer. So, you need to address them directly.
But, you know, you really need to let tutors know that. I think that is
important that you really need to let your tutors know that they need
to go and ask students the questions. (Unit E, UC)
Findings 233
This example also emphasised the importance (and responsibility) of unit coordinators
to mentor their tutors around questions related to pedagogy and teaching methods. The
majority of comments about paying attention to the teaching methodology were made
by staff involved in Unit E who were all international academics and researchers, with
long teaching experience. This quote also points towards the importance of teacher
training being available to all staff (including sessional academics), the role someone’s
overall research background plays in teaching confidence (as they were all discipline
experts), and the impact of previous personal experience on their perceptions. This
impact became even more salient with academics sharing their opinions about “correct
ways” of learning:
To me, the correct way of going about this, as a student, and I say
this as a somebody who was a student 15 years ago, would be ‘Here,
there is a prep material that is saying that I am expected to know this
from school’s Maths B. I don’t know this from school Maths B, so I
go through the slides see if I can figure out anything. If I don’t, I
would go to the STIMulate support session, or drop in to STIMulate
saying ‘Hi, I am a Unit S student, I need help with this material
before the lecture, so when it comes to the lecture, I will get that
expected level of knowledge’. (Unit S, UC)
This opinion was expressed by an academically successful graduate, who, most
probably, was well-motivated to learn, and had developed a set of specific (and
effective) learning strategies. Yet, a point needs to be made about the level of
consciousness in developing such strategies. There is a possibility that the above-
described positive experience was based on participants’ “trial and error” method of
learning rather than resulting from an educated decision about strategically using
effective learning strategies. Moreover, the comment was made 15 years after
graduation, which indicates that the respondent, an active teaching academic, during
post-graduation professional activity gained important knowledge about learning and
teaching in the higher education context.
Data also revealed that participants were aware of the “trial and error” method
often applied by students and the need for more training in pedagogical approaches to
be able to assist their students with learning strategies. One respondent remarked:
234 Findings
“They want to solve the problem straight right, without even reading the instruction
until the end” (Unit E, Respondent 3), while another stated: “Yeah, they just do the
same thing over and over again, they learn in the same way” (Unit E, Respondent 2).
One participant expressed this in a more developed manner:
You see, when I speak with them very often they tell me frankly:
‘Look, we learn by trial and error, because very often we do not
know how to learn within this environment’. We deliver classes in
blended learning mode which is very challenging not only for us but
for students as well. There is an online part and the face-to-face part,
both requiring different learning skills and strategies. And we do not
teach students about these strategies’. (Unit E, Respondent 2)
Interestingly, this quotation pointed towards the need to educate students about
effective learning strategies; however, nothing was mentioned about a similar need for
enhancing academics teaching staff’s awareness of effective teaching and learning
strategies. Furthermore, when asked about the suggestion of developing resources and
directly teaching learning strategies, academic teaching staff presented a split opinion,
which was consistent with students’ reactions to the same suggestion (section 4.3.8).
The academic teaching staff’s diversity of opinions ranged from hesitation and
scepticism about the effectiveness of such an approach, to acceptance and support for
the idea. For instance, one participant stated:
I don’t know, I am a bit apprehensive. I think that would be a bit too
much of spoon-feeding. Because again, they will have everything
available on the spot. They would never go back and look by
themselves, on the Internet to go and see what resources are
available. I think that is a risk if you give them too much. You see, I
give them this sheet [shows a learning activity], and then I tell them
to go back to the lecture and to seek how to solve this problem. They
can also go to the online resources. But if I will tell them – perhaps
you should go and look at the lectures and learning material, I think
it’s obvious and we should not be doing this. That’s too much. (Unit
E, Respondent 2)
Findings 235
This example illustrates an already identified need for raising awareness of pedagogical
principles of learning design. It appears that participants’ concerns resulted from a
misunderstanding about the concept of learning strategies, preventing them from
seeing such an activity as a way of empowering students in becoming self-regulated
and self-directed, active learners. Instead, it appears that this academic respondent
focused on the what, a technical aspect of learning strategies, providing information
about the steps to take. The why and how were missing, that is, an explanation of the
reasons why the learners should take these steps and advice on how these steps should
be taken were missing. Such information could be presented to students in the form of,
for instance, retrieval practice activities, a learning strategy the academic respondent
may not have been aware of.
On the other hand, another participant remarked:
That would probably be super helpful, especially at the beginning in
early years. […] And that’s because of this entire “teach yourself”
those kind of things, you can look up online sort of things that would
work for you. […] And having that kind of information available
from something that is backed up somehow would be quite helpful.
It’s still just a matter of students who wouldn’t even open it. (Unit S,
Respondent 1)
Interestingly, different opinions were again expressed by participants in different
disciplines, and, most importantly, with different personal learning and teaching
experiences, with international academic teaching staff being more sceptical towards
the suggestion than their domestic colleagues.
Overall, the data analysis made salient another, more generic problem
interwoven in participants’ responses across all data: a low level of awareness of
learning design principles underpinning task design. For instance, when reflecting on
the learning affordances offered by the LMS’ adaptive release functionality (see section
1.9) implemented in Unit S, one participant noted:
236 Findings
My concern with the adaptive release is that, if the students have no
familiarity with what is in the adaptive release material, they
probably are going to go and say ‘I don’t have time to go and learn
this now’. […] So, for some students, perhaps even for those students
who needed it most, rather than it being a gentle introduction to the
topic, it’s a roadblock to getting the extrinsic knowledge of lecture
slides. (Unit S, Respondent 3)
It appears that this participant did not implement strategies to explain the purpose of
the adaptive release, and how to use this tool as a preparatory activity to enhance
students’ learning (see section 4.2.2.). Yet, when initially designing and developing
activities supporting the application of the adaptive release, a considerable amount of
time was spent by a learning designer and the unit coordinator on discussing teaching
strategies to be used by the teaching team to encourage uptake of learning affordances
offered by this functionality and to facilitate students’ experiences. It appears that two
years after the original design this knowledge had not been passed on.
In summary, a different model of collaboration between educational
professionals (e.g., academics, learning designers, curriculum designers, researchers
within education) may be needed: i.e., a model that would raise academic teachers’
awareness of pedagogical approaches and the effective teaching and learning strategies
to be applied in the particular contexts of their disciplines, and in this way empower
them to effectively assist students in becoming self-regulated and self-directed,
autonomous learners.
4.4 REFLECTION ON RESEARCH FINDINGS
This chapter has presented the results of an in-depth analysis of the architecture of
identified learning networks, the ways they were used by student respondents, and
academic teaching staff’s perceptions of their architecture and use by students.
In response to research question one (section 4.1), which investigated the
architecture of learning networks and networked learning practices (Carvalho &
Goodyear, 2014a), the data analysis revealed four overall findings.
First, this study confirmed the co-existence of two types of learning networks that
form learners’ personal learning environment (PLE) (Casquero et al., 2013, 2016;
Findings 237
Jones & Issroff, 2005; Orlikowski, 2002; Tolmie & Boyle, 2000): formal, institutional;
and informal, personal learning networks.
Second, this study revealed interesting patterns of perceiving and taking up
learning affordances offered by learning networks, also revealing a relationship
between identified patterns and the type of learning network. These identified patterns
provide insight into the ways the learning designs of the two units under investigation
were used by both learners and academics. The implications of this finding are
discussed in section 6.4.
Third, the concept of a productive learning network stipulates that for a learning
network to be productive, it needs to enable shared learning, which results from a
collaborative, coordinated and purposeful activity of co-creating knowledge (Carvalho
& Goodyear, 2014a). This study revealed that the potential of identified learning
networks to be productive was not fully perceived and taken up, as it appears that many
respondents were using their networks to merely “consume knowledge” (Kop & Hill,
2008, p. 2), or assimilating it, as opposed to co-creating it (Kop & Hill, 2008, p. 2).
Finally, the fourth overall finding pointed towards the low level of awareness of
self-directed learning skills of this group of first year, undergraduate STEM students.
In summary, it appears that the learning design of the two units under
investigation, despite of the conscious effort to “thoughtfully integrate” (Alammary,
Sheard & Carbone, 2014, p. 443) appropriate pedagogical approaches and foster the
development of self-directed learning, only partially realised this aim. As a result,
further investigation of relations, connections and inter-dependencies between
respondents’ personal backgrounds (i.e., frames of references) and their practices (i.e.,
uptake of opportunities for learning) was initiated.
The analysis continued in the section 4.2 by outlining the data collected in
response to research question two about respondents’ networked learning practices.
Two overarching findings were revealed.
First, the networked learning practices were requirement-driven, oriented towards
passing the assessment. The networks were used to consume knowledge, with
relatively limited connectivity and interaction, occurring primarily within formal
learning networks.
Second, student responses indicated low levels of awareness of effective learning
strategies, which may lead to the illusion of competence (Castel, McCabe & Roediger,
2007), giving students a false impression of the way they manage their learning
238 Findings
practice, which in turn can slow down or, in some cases, prevent the development of
self-regulated learning skills.
In summary, the data analysis provided further evidence of the importance of
functional context for the learning design. The findings indicate that the learning
activities did not encourage collaboration (e.g. absence of required group-work) and
did not explicitly require students to work together to de-, re- and co-construct
knowledge. In short, the need for co-construction through direct collaboration was not
created.Thus, low levels of connectivity and interaction result from the ways learning
activities were designed. For this reason, the identified networked learning practices
could not promote the development of respondents’ epistemic fluency, which is
founded on the concept of actionable knowledge.
The findings related to research question two also attracted attention to the
importance of academic teaching staff’s knowledge of pedagogical principles
underpinning blended learning approaches, and their preparedness to teach within this
context and to advise learners on effective learning strategies.
These findings were presented in section 4.3, in response to research question
three. Two overall findings were formulated: 1) the importance of academics’ frames
of reference for the process of learning design; and 2) the need of raising pedagogical
awareness of academic teaching staff. Both findings pointed towards interference of
perceptions as a phenomenon that illustrates well the differences in perceiving the
affordances for learning, assessing their importance, and estimating the intensity and
frequency of their uptake by both students and academic teaching teams. As a result
of the interference of perceptions, the need for raising academic teaching teams’
awareness of both the importance of having (some) insight into students’ frames of
reference (e.g., previous knowledge) and pedagogical approaches was also formulated.
The next chapter discusses the findings from the perspective of the theoretical
underpinnings presented in section 2.0, with the overall aim to assist STEM
undergraduate students in becoming active learners, as defined by Bjork, Dunlosky
and Kornell (2013) (section 1.5).
Discussion 239
Discussion
This chapter discusses the findings presented in Chapter 4, in light of the theoretical
underpinnings outlined in Chapter 2. The chapter systematically addresses three
research questions, culminating in five conclusions. Building on ecological
perspectives of human learning such as Bronfenbrenner’s Bioecological Model of
Human Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006), with
special attention paid to the architecture of learning networks (Carvalho & Goodyear,
2014a) and Personal Learning Environments (PLEs) (Casquero et al., 2013, 2016;
Jones & Issroff, 2005; Orlikowski, 2002; Tolmie & Boyle, 2000), which are seen as
an ecology of formal and informal learning networks, this chapter first discusses three
types of relationships identified through an in-depth data analysis of research question
one (section 5.1). The discussion provides evidence of the importance of the proximal
processes (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) (section 2.1.1)
occurring within microsystems and the negative consequences of disjuncture,
(Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006; O’Toole, Hayes &
Halpenny, 2020) (section 2.1.1), or lack of connectivity between different subsystems,
impeding student learning.
Next, the discussion in section 5.2 focuses on findings related to students’
networked learning practices revealed in response to research question two. As this
study considers self-regulation (Zimmerman, 2002) and self-direction (Cleary &
Zimmerman, 2000; Zimmerman, 2002; Eva & Regehr, 2005; van Meeuwen et al.,
2013) as the core elements of active learning, particular attention is paid to an
interpretation of the findings from the perspective of the above-mentioned concepts,
including the context of online learning (Cohen & Magen-Nagar, 2016; Kizilcec,
Pérez-Sanagustín & Maldonado, 2016, 2017; Magen-Nagar & Cohen, 2016; Lee, Lee
& Watson, 2019). This section also presents the relationship between the concept of
illusion of competence and low levels of students’ awareness of effective learning
strategies.
In section 5.3, the findings to research question three are discussed, with a focus
on the identified phenomenon of interference of student and academic teaching staffs’
perceptions and its consequences for both designing learning tasks that use self-
240 Discussion
regulation and self-direction, and for the application of connected learning ideas in the
curriculum. Next, the academic teaching teams’ tendency of making assumptions is
discussed, followed by a call for changes to be made in academic teaching staff’s
pedagogical readiness. Overall, the role of the academic educator in the context of
connected learning environments, composed of formal and informal learning
networks, is revised, and a new term, expert in the network, is proposed to describe
this revised role. This section culminates by proposing a new ecological curriculum
and learning design framework of connected epistemic domains (Markauskaite &
Goodyear, 2017a) as a way of enabling diverse experts (i.e. academics, learning
designers, industry partners, students, etc.) to co-create an environment which would
provide opportunities for learning, applying and practising epistemic fluency
(Markauskaite & Goodyear 2017a; 2017b; 2018a; 2018b).
The chapter closes with a reflection that introduces conclusions and leads to
implications.
5.1 RESEARCH QUESTION 1: WHAT LEARNING AFFORDANCES OFFERED BY PRESUPPOSED LEARNING NETWORKS WERE PERCEIVED AND TAKEN UP BY UNDERGRADUATE STEM STUDENTS?
In response to this research question, the students revealed low levels of awareness of
the following three factors: 1) the architecture of respondents’ learning networks; 2)
the potential for learning that the networks offer in the form of learning affordances;
and 3) the ways of taking up these affordances. The findings related to the first research
question clearly indicated a low level of student agency in constructing and using their
learning networks. That is, the study revealed that, for learning purposes, respondents
were differentiating between formal and informal types of learning networks,
privileging formal, institutionally designed learning networks. The data also indicated
that respondents, rather than interacting with a broad range of social agents that would
include professionals and/or family members to co-construct new knowledge, and in
this way make the networks productive, instead preferred to use networks to assimilate
knowledge from a small and selected number of social agents including university
teaching staff and fellow students enrolled in the unit (section 4.1.2.2). However,
research indicates that learning networks form an ecosystem of entities that should
promote co-creation of knowledge, as opposed to mere knowledge assimilation that is
critically evaluated and fed back to the distributed environment. As observed in section
Discussion 241
2.6, learning is a continuous formation and removal of connections in a network
between its entities. This cyclical process starts with personal knowledge provided to
the network, which also provides information that is recognised and used to co-create
knowledge, which is then fed back into the network (Downes 2008; 2010a; 2012;
2019; Siemens, 2004; 2005a; 2006; 2019; n.d.). Such an ecosystem, or learners’
Personal Learning Environments (PLEs) (section 2.5) within it, needs to be owned by
learners. That is, the ecosystem needs to enable interaction within a distributed
environment (Anderson & Dron, 2011), foster distributed learning process(es) (Duke,
Harper & Johnston, 2013) that are purposeful and conscious (i.e., realised by the
learner) (Casquero et al., 2013, 2016; Jones & Issroff, 2005; Orlikowski, 2002; Tolmie
& Boyle, 2000).
For a learning network to be productive, the process of shared learning that results
from the collaborative, coordinated and purposeful activity of co-creating knowledge
(Carvalho & Goodyear, 2014a) and feeding it back to the network (section 2.2) needs
to take place. Data analysis suggests that the identified learning networks of the student
respondents were not fully productive, as they seemed to support only partially
purposeful, collaborative co-creation of knowledge. That is, some affordances were
perceived and taken up; however, it appears that these were predominantly provided
to respondents through formal learning networks, with low requirements for learners’
agency. Furthermore, it also appears that some student respondents preferred to
consume the knowledge without feeding it back to the network; they simply did not
share their knowledge with their classmates and/ or peers.
The data analysis presented in Chapter 4 revealed three types of relationships that
evidenced low levels of learner ownership and agency. From the perspective of this
study, the identified relationships have the potential to explain why the learning
networks were only partially productive:
1. The type of learning network, the functional context, and the uptake of learning
affordances. It appears that learning affordances offered by formal learning
networks were taken up in the first instance, followed by the affordances
provided by informal learning networks.
2. The type of learning network, frames of reference, and the type of ecological
system (Bronfenbrenner, 1979; 2005) the social agents belonged to resulting in
242 Discussion
disjuncture (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) due to
low levels of interaction between the subsystems. That is, in the first instance,
the affordances offered by social agents belonging to the microsystem were
taken up, followed by those offered by the mesosystem and, finally the
exosystem. This finding also supported the validity of the first relationship.
3. The type of learning network, the type of online tool, and the required level of
autonomy when using the tool. It appears that, in relation to online learning
tools, an important contributor to the uptake of the affordances they offered
was the level of autonomy required by the tool. The data analysis revealed that
uptake of the online tools belonging to the formal learning networks, and
requiring a low level of autonomy was privileged over those requiring a high
level of autonomy and belonging to informal learning networks. In addition,
the data demonstrated that the decisive factors impacting on perception and
uptake of affordances for learning was the required level of autonomy (thus,
an element of functional context), not the type of intended interaction afforded
by the tool. These relationships, and implications ensuing from them, are discussed in the next
section.
5.1.1 Relationship between the type of learning network, the functional context and the uptake of learning affordances
This section discusses the first identified relationship based on the finding that the
elements of formal learning networks that require low levels of autonomy, were
perceived and taken up first with high frequency (i.e., had a high rate of uptake) and
intensity (i.e., vigorously taken up), when compared with those of informal learning
networks, which required medium to high levels of autonomy. Due to the emergent
nature of learning (Goodyear & Carvalho, 2014a; 2014b; 2016), special attention
needs to be paid to the environment and conditions that stimulate the process of
performing a learning activity by taking steps to complete a learning task.
Affordance, as used in this study and defined in section 2.2, is a unit of analysis,
embedded in a functional context that, in its turn, is embedded in a frame of reference.
Functional context could be broadly described as a learning environment, that is,
Discussion 243
formal learning networks of units under investigation, that create a need for taking up
a learning affordance. The functional context facilitates the perception of learning
affordances and, by hosting learning needs, the functional context triggers uptake of
learning affordances (Czaplinski, 2012). Section 1.8 presented an in-depth description
of functional contexts of both units under investigation. It was demonstrated that the
functional contexts, designed with the aim of effectively assisting students with
learning discipline knowledge and with becoming active learners, were in fact not
successful in achieving this aim. This provided evidence that the functional contexts,
in both units, only partially fulfilled their triggering role. It appears that the need for
learning created by the functional contexts only made salient the learning affordances
embedded in formally designed learning networks, which required low levels of
autonomy.
However, a need for learning differs from an opportunity for learning. The need
is a form of an obligation, is salient and well visible to learners, embedded in a
functional context, in the form of, for example, assessment tasks. Thus, the probability
that learners will perceive the learning need, and will take it up is high. An opportunity
for learning, on the other hand, depends on more subtle interdependencies influencing
its perception and uptake. The functional context cannot fulfil its triggering role in
encouraging uptake of opportunities for learning if it is not supported by mechanisms
that raise learners’ levels of agency. Learners need to show a proactive attitude towards
their own learning, to consciously perceive and to take up an opportunity for learning.
Within the functional context that emphasised learning needs and required low
levels of autonomy, inexperienced university learners in this study (see section 3.2.3
for demographics) had difficulties with actively searching for, locating, and realising
the value of learning opportunities. Thus, as demonstrated across the previous chapter,
respondents had low levels of awareness of learning affordances offered by informal
learning networks, as these affordances were embedded in the functional context as
opportunities, not needs. This leads to two overall conclusions. All of the above points
towards a relationship between frames of reference, impacting on perception of an
opportunity and/or a need, functional context (learning environment creating an
opportunity and/or a need), learning affordances (occasions to satisfy an opportunity
and/or a need) and their uptake.
244 Discussion
First, it appears that the definition of affordance, as presented in section 2.2,
should include a nuanced distinction between a need for learning and an opportunity
for learning. This study observed the first type of learning affordance to be more often
present in formal learning networks, and in consequence, it was more often perceived
and taken up by learners. The second type of learning affordance seemed to be more
often present in informal learning networks, and was more difficult to be perceived
and taken up by student respondents.
Second, it appears that learners’ ownership of their PLEs was insufficient, and
manifested by the above-identified pattern of uptake of learning affordances. The
findings provided evidence that students preferred using online learning tools that were
part of formal learning networks (section 4.1.2.3), set up by academic teaching teams
as part of the functional contexts, and requiring limited levels of autonomy. The online
tools in informal learning networks required high levels of autonomy and were
perceived as the least important, and were thus the least frequently and intensely used
(see section 4.1.2.3.3.1 for details). As observed in sections 2.5 and 5.0, a PLE needs
to be owned by learners. The evidence presented in previous chapter strongly suggests
that only a part of learner PLEs was used for purposeful and conscious distributed
learning, which indicates students’ relative lack of skills in building and owning their
PLEs.
Finally, it is important to note that the functional contexts were designed by
educators who themselves had their own perceptions and beliefs about learning
strategies and pedagogical approaches. This may have impacted on the ways certain
affordances were made more salient than others, which then may have influenced
students’ perceptions and uptake. This finding is all the more important, since the
learners’ frames of reference also impacted on their perceptions of the learning
affordances, creating an effect of interference of perceptions (see section 4.3 and
section 5.4).
This finding presents consequences for learning design, especially from the
perspective of the ways learners complete their tasks, as it illustrates, to some extent,
the phenomenon of emergence, a key element in indirect learning design (Goodyear
& Carvalho, 2014a; 2014b; 2016; Jones, 2015). To raise awareness of the learning
affordances, to increase their uptake, and to enhance learners’ agency (which leads to
increased autonomy), it is not enough to create a functional context (i.e., learning
network) rich in opportunities for learning. It is also important to influence learners’
Discussion 245
frames of reference to stimulate the perception (and the realisation) of either the need
or opportunity to take up learning affordances. This observation leads to the second
identified relationship discussed below.
5.1.2 Relationship between the type of learning network, frames of reference,
and the type of ecological system
Davidson and Goldberg (2010) have argued that networked learning, enabled by
modern, digital tools, creates many opportunities for diverse social agents to
participate in, and collaborate within, the learning process. Digital learning is about
connecting, fostering relationships and interacting with “myriad strangers” (Davidson
& Goldberg, 2010, p. 5) to co-create knowledge. The findings indicate that, within the
context of this study, the connection with numerous interlocutors did not occur. Quite
the opposite, the connections, and in consequence the interactions actioned by learners
were predominantly pragmatic, directed towards a narrow group of selected social
agents, and they privileged co-creation of knowledge only to consume it later (e.g., for
assessment purposes), without feeding it back to the network. This finding applied to
both the digital (i.e., online elements) and the non-digital (non-online) elements of
learning networks, such as ideas, people and artifacts. Furthermore, the study provided
evidence, presented in section 4.1.2.2, that the identified learning networks of students
were composed predominantly of a limited number of elements that were used
following the same pattern, in both formal and informal types of learning networks.
That is, a mapping of the categories of social agents identified for the needs of the
study (sections 4.1.2.2.2 and 4.1.2.2.3), and based on Bronfenbrenner’s Bioecological
Model of Human Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris,
2006), revealed a pattern of perceived importance and intensity of uptake of
affordances indicating that respondents, within the formal learning network, would
first turn towards experts, members of the respondents’ mesosystem (i.e., academics),
and next to their peers, members of their microsystem. Next, they would ask for
assistance from social agents in informal learning systems, with members of their
microsystems approached first (e.g., family members), followed by members of the
mesosystem (e.g., classmates, other students enrolled in the degree regardless the year
of enrolment). Finally, members of their exosystem (e.g. professionals who could be
accessed through online tools belonging to informal learning networks, such as
246 Discussion
specialised blogs or sites) would either be approached sporadically in the third
instance, or not approached at all.
This finding illustrates how the functional context of timetabled activities
triggers the uptake of learning affordances, including the ways other social agents,
members of formal learning networks, are perceived and approached for learning
purposes. In short, the finding revealed that learners privileged collaboration to co-
create knowledge with social agents they trusted and considered to be experts, such as
academics and close peers. Furthermore, the finding also illustrates the concept of
disjuncture (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006; O’Toole, Hayes
& Halpenny, 2020) (section 2.1.1), which draws attention to a lack of connections
between subsystems, impeding on the quality (and effectiveness) of interactions and
posing roadblocks to learning by disturbing the transfer of knowledge.
This raised questions about a more complex problem of the productivity of
learning networks (Carvalho & Goodyear, 2014a), which relates to the quality of
learners’ interactions, including the process of feeding back to the network (e.g., by
assisting peers/ classmates in learning). The concept of a productive learning network
was discussed in sections 1.5 and 2.2. To be productive, a learning network needs to
promote the process of shared learning that results from the collaborative, coordinated,
and purposeful activity of co-creating knowledge (Carvalho & Goodyear, 2014a) and
feeding it back to the network. The findings provided evidence that the process of co-
creating knowledge was only partially collaborative and coordinated, and followed the
above-identified pattern of learning from/with selected collaborators, who were
chosen according to personal perceptions and had an affiliation with only one of the
ecological systems. In addition, the process of feeding back the knowledge to the
network was also limited, dominated by knowledge assimilation with not so frequent
claims of sharing knowledge with others (section 4.1.2.2.3 and 4.1.2.3.3.2). As for the
attribute of purposefulness, the learning was often directed to satisfy pragmatic needs
such as submitting an assessment, passing an exam or successfully completing the unit
of study (section 4.1.2). Although the purpose of successfully completing an
assessment is fully understandable and recognised in education as a strong motivator
to engage in co-creation of knowledge, the ultimate purpose of learning networks is to
assist learners to become active, independent learners. Merely wanting to pass an
assessment is not the type of motivation that can assist with developing learners’ self-
direction and/or self-regulation in the long term, as it is focused on imminent reward.
Discussion 247
Interestingly, this finding goes against findings in sections 4.1 and 4.2, which
indicated, on several occasions, student respondents’ willingness to learn as a strong
motivation either driving them to undertake the degree and/or to engage in
collaborative learning. It appears that the respondents, finding themselves in a
particular functional context, in order to satisfy the need created by the functional
context, were influenced by their frames of reference to find assistance/collaborators
to firstly satisfy the need for learning created within a formal learning network, and
next capitalise on the potential for learning, offered by an informal learning network
(section 4.1.2). Depending on the type of functional context (e. g., timetabled activity)
and in response to the created need for learning, the respondents preferred to learn
from experts (academics) and/or peers (e.g., during lectures), as opposed to learning
with experts/peers (e.g., during workshops). It is important to note that, regardless of
the type of timetabled activity, the social agents from/with whom respondents
preferred to learn (i.e., experts/peers) most often belonged to the respondents’
mesosystem. Furthermore, the data also showed an emerging pattern of dependency
between the type of functional context and learner preferences. It appears that when
learners associated the learning activity with a particular approach to teaching (e.g.,
lecture vs workshop), their preference changed accordingly, showing willingness to
learn from experts when the functional context privileged attendance (e.g., lectures).
When the functional context privileged more active participation (e.g., workshops),
learners preferred to learn either from or with either experts or peers (section 4.1.2.1).
This finding was confirmed by the data analysis of students’ uptake of learning
affordances offered by different social agents in formal and informal learning networks
(section 4.1.2.2). Students preferred to learn with peers in the first instance (i.e., with
friends, members of their microsystems), and next they preferred to learn from experts.
This has important consequences for learning design, which increasingly emphasises
a student-centered approach to teaching and learning. First, should certain types of
learning and teaching approaches that create a need for learning in a certain way (e.g.,
lectures, learning from) be reconsidered from the perspective of learning networks and
networked learning to promote more autonomous learning? For instance, should face-
to-face lectures continue to be offered to students or should they be replaced by online
vodcasts that would satisfy the same need (learning from an expert)? In addition, if
embedded in a functional context that would make the learning opportunities more
salient and would directly attract learners’ attention to them (e.g., by teaching
248 Discussion
explicitly learning strategies), such an approach could prove beneficial for promoting
the concept of PLEs to learners. As observed in section 2.5, the concept of PLEs is a
key concept for active and autonomous learning, as it describes a range of applications
that enable a shift from an authoritative, institution-controlled digital system to the
open “wisdom of crowds” (Madden & Fox, 2006 p. 2).
For learners to take ownership of their PLEs, they need to realise the value of
their learning networks, to take control of them and to become autonomous. This point
is discussed in more detail in the next section.
5.1.3 Relationship between the type of learning network, the type of online tool
and the required level of autonomy when using the tool
Section 2.2 presented the concept of indirect learning design, resulting from the
emergent nature of human learning. Goodyear and Carvalho (2014a; 2014b; 2016)
pointed towards the difference between task and learning activity, describing the first
as “suggestions of good things to do” (Goodyear & Carvalho, 2014b, p. 60), composed
of steps, instructions, and suggestions; and the second as a state of being active, taking
action to complete steps, instructions. Both elements combined eventuate in
completion of the task and ultimately (if indirectly) learning.
Indirect in nature, a learning activity is a mediator between designed tasks and
the outcome of the activity of completing the task (Goodyear & Carvalho, 2016). It
appears that the affordances that offered the potential to satisfy the need for learning,
created by the functional context (and requiring a low level of autonomy), were
perceived as more important and taken up more frequently and intensely. For instance,
section 4.1.2.3 demonstrated that the affordances that offered the potential for
satisfying the opportunity for learning created by the functional context (and requiring
a high level of autonomy) were perceived and taken up less frequently and intensely.
This finding clearly indicates certain patterns in the ways learners were using online
tools. Thus, they approached the completion of learning activities depending on the
properties of the learning network, which encompassed the type of the learning
network (formal vs informal), the functional context (need vs opportunity), and the
nature of the potential for an action (required vs optional). With the increased
prescriptiveness of the functional context (formal, required, need) the impact of
learners’ frames of reference decreased, which was manifested in low levels of
Discussion 249
autonomy. With a decrease in prescriptiveness of the functional context (informal,
optional, opportunity) the impact of learners’ frames of reference increased, which was
manifested in high levels of autonomy. It appears that the student respondents
preferred to take up affordances that would satisfy their required learning needs,
embedded within formal learning networks (as opposed to taking up opportunities for
learning).
Overall however, it is important to be conscious of the specific challenges of
this study, given that it is based on self-reported claims from volunteering participants.
The study design made it highly difficult to critically verify participant’s claims to
establish a more objective picture of the ways they were using learning networks and
were practising networked learning. Different options were sonsidered, such as, for
example in-depth interviews/ focus groups over a certain period of time. However,
these were not used, as they may have required from participants an important
investment in time and engagement. There was also in increased risk of coercion due
to the fact that the personal relationship might have been developed between me and
the particpants, increased anxiety caused by the fact of participating in a time-
consuming study and increased risk of inconvenience. Thus, the decision was taken to
not to use the above-mentioned data collection techniques. This poses important
limitations to both conclusions and potential implications of the study, including
further research. Notwithstanding these limitations (discussed in more detail in section
6.3), it is still possible to make some more general observations that may serve as an
indicator for further reflection and investigation within the domain of learning design.
From the perspective of learning design (Goodyear, 2015; Konnerup, Ryberg
& Sørensen, 2018), the identification of a pattern of students’ decision-making
processes in taking up affordances for learning, the ways they were using learning
networks and constructing their own PLE(s) may inform learning design, as it can
“help the reader understand enough about learning and educational issues that they can
be adapted and redesigned for her [the reader’s] own practice” (Konnerup, Ryberg &
Sørensen, 2018, p. 334). It seems that, in the educational context, learners were taking
up these learning affordances offered by the micro- and mesosystems in the first
instance, followed by those offered by the exosystem. Moreover, it also appears that
there was a relationship between the types of ecological systems, and whether they
required a level of autonomy and uptake of learning affordances. That is, the closer the
type of ecological system was to the learner, the lower the requirement for autonomy.
250 Discussion
In other words, it appears that those learners who claimed to use their formal learning
networks as the core component for building their PLEs were, in fact, less autonomous
(hence less active) than those who used the elements from their exosystem to construct
their PLEs (see findings in section 4.1). As observed in Chapter 2 (section 2.5),
research on PLEs (Downes, 2010a, 2010b, 2012, 2015) has shown that PLEs are
spaces (digital and non-digital) composed of (predominantly) free, online learning
resources, where the learner takes control of the ways the resources are managed, and
also actively engages with the distributed environment (people, networks), while co-
creating knowledge (as opposed to only consuming knowledge). This is a dynamic,
distributed process that occurs through interactions with networks of people, services,
and resources. Hence, to become active, learners need to develop their connectedness,
the capability to connect within formal and informal learning networks, and to take
control when taking up the learning affordances, regardless of the type of network and
how it fits into learners’ ecosystems. That is, from a learning design perspective, it
appears that the need (not just the opportunity) for learning should be created within
both types of learning networks and across all ecological systems. For this to happen,
the learning design would need to impact on a learner’s perception to assist them in
perceiving learning networks as media of actionable knowledge (Duke, Harper &
Johnston, 2013), resulting in change (Downes, 2010a; 2010b; 2012; 2015), and
manifested by co-construction and feeding back of knowledge.
Such a shift would also require change in networked learning practices,
which are discussed in relation to the next research question.
5.2 WHAT NETWORKED LEARNING PRACTICES ARE OCCURRING
WITHIN THE PRESUPPOSED LEARNING NETWORKS?
Section 2.5 presented a definition of networked learning practices as a situation (Dohn
et al., 2018) of interactions that result from connections (digital and non-digital)
between different social agents (De Laat & Ryberg, 2018; Goodyear, Banks, Hodgson
& McConnell, 2004; Jones, 2015). Productive networked learning is characterised as
a learning activity that aims to allow learners to complete a learning task and
culminates with the co-construction and co-creation of knowledge. This last point is
very important as it emphasises the quintessence of networked learning – the
collaborative “production of knowledge”, as opposed to knowledge assimilation. To
Discussion 251
be fully productive, networked learning practices should feed the produced knowledge
back into the network, to be shared with other social agents (Downes, 2010a, 2010b,
2012, 2015).
In response to research question 2, the findings revealed respondents’ low
levels of awareness related to two types of challenges that effectively prevented them
from developing productive networked learning practices: 1) self-regulated learning
skills, foundations of effective learning strategies that may lead learners to develop an
illusion of competence or “a discrepancy between predicted and actual [memory]
performance” (Castel, McCabe & Roediger, 2007, p.107); and 2) self-directed learning
skills. Combined, both challenges provided evidence of respondents’ difficulties with
“owning” their PLEs. The next two sections discuss in more detail both identified types
of challenges within respondents’ learning practices and their impact on respondents’
networked learning.
5.2.1 Low level of awareness of effective learning strategies
This study has demonstrated that student respondents were not familiar with, and/ or
aware of effective learning strategies that could improve their learning practices, which
often resulted in students using trial and error approaches to their learning. This is
consistent with research in applied cognitive psychology (Dirkx, Camp, Kester &
Kirschner, 2019; Hartwig & Dunlosky, 2012; Morehead, Rhodes & DeLozier, 2015)
indicating undergraduate students’ “suboptimal use and awareness of effective study
strategies” (Dirkx, Camp, Kester & Kirschner, 2019, p. 953). The application of trial
and error strategies, may, in turn, lead to the illusion of competence (Castel, McCabe
& Roediger, 2007; Koriat & Bjork, 2005; Soderstrom, Yue & Bjork, 2016), giving
students a false impression of the way they manage their learning practice, slowing
down or, in some cases, preventing the development of self-regulated learning skills.
In other words, the low level of awareness may have impacted on some students’ need
for improving their learning skills, producing a vicious circle effect, in turn leading to
learners overestimating their confidence in their memory performance and reducing
their opportunities to become active learners. This finding is in line with numerous
findings in the field of metamemory (Dunlosky & Tauber, 2016; Kornell, Rhodes,
Castel, & Tauber, 2011; Roediger & Pyc, 2012; Soderstrom & Bjork, 2015; Winne &
Jamieson-Noel, 2002) investigating learners’ beliefs about their own learning
processes and the learning strategies they are using. This study has indicated that
252 Discussion
students’ low levels of awareness of self-regulation and self-direction impacted on
their capability to develop productive PLEs and jeopardised their potential to become
active learners.
The critical analysis of the findings, compared with the three phases of self-
regulation, as described by Zimmerman (2002) (see Section 2.9.2), provided evidence
of student respondents’ uneven learning practices, with some aspects of self-regulatory
learning present (with varying degrees of achievement), and others absent. The next
sections discuss the findings in relation to the three phases of self-regulation, in line
with Zimmerman’s model (2002).
5.2.1.1 Phase 1 in Zimmerman’s model of self-regulation
With regards to Zimmerman’s Phase 1 in self-regulation (Forethought), this study
found evidence of respondents setting specific goals to be achieved and strategic
planning. However, the ways of achieving the goals and completing the plans seemed
to pose a challenge. For instance, section 4.2 demonstrated that respondents did have
a study routine (i.e., they were setting study goals and planning their learning);
however, they were lacking operational skills (“how to”) that would enable them to
meet their set goals and implement their learning plans. Furthermore, section 4.2.2
(Figures 4.2, 4.3) clearly showed respondents’ difficulties with effectively estimating
the amount of time they needed for studying, as well as confusion related to critical
assessment/ adjustment of their own estimates (section 4.2.2. Figures 4.4 and 4.5). This
is an important finding for learning design, as the empirical evidence from research on
self-regulation within online environments (Kizilcec, Pérez-Sanagustín & Maldonado,
2016; 2017; Lee, Lee & Watson, 2019) indicates that goal setting and strategic
planning are important indicators of learner engagement (e.g. completing assessment,
watching recorded lectures) and academic achievement (Artino, 2007).
Moreover, this finding made salient the lack of operational skills (i.e., effective
application of learning strategies) assisting respondents in focusing on the learning
process itself, as opposed to focusing on task completion only. All findings in section
4.2 clearly indicated low levels of student respondents’ awareness of effective learning
strategies, which poses another challenge to learning design teams. Literature on self-
regulated learning within online environments (Cohen & Magen-Nagar, 2016; Lee,
Lee & Watson, 2019; Magen-Nagar & Cohen, 2016) suggests that learning strategies
Discussion 253
play an important role in increasing motivation and sense of achievement of learners
using online environments for studying.
In relation to respondents’ own views on their self-efficacy and competence,
respondents’ uneven and confusing attitudes could be observed across the whole of
section 4.2. In some instances student respondents indicated a low sense of self-
efficacy and competence (see section 4.1.2.2.3 on providing assistance to other
students, or section 4.1.2.3.3.2 on contributing to online resources), only to be
contradicted in other responses where many students felt competent and had a
relatively high belief in their self-efficacy (section 4.2.5). However, research
(Littlejohn et al., 2016; Lee, Lee & Watson, 2019) has provided evidence of a direct
relationship between self-efficacy and familiarity with learning tasks to complete. This
indicates that confident learners, who own their PLEs, will most probably be able to
see beyond formal learning networks and perceive more nuanced learning affordances
that offer the potential to satisfy both needs and opportunities.
5.2.1.2 Phase 2 in Zimmerman’s model of self-regulation
With regards to findings related to the second Phase in Zimmerman’s model
(Performance), this study has provided further evidence of students’ uneven and
confusing approaches to learning. That is, section 4.2 indicated that student
respondents made an effort to apply some strategies, such as time management,
environmental structuring in terms of organising one’s learning space, and self-
instruction, but only to some extent and inconsistently. This study also found that the
catalogue of these strategies was limited and their application was based on the above-
mentioned trial and error approach rather than a well-structured, planned approach to
learning. However, research has demonstrated (Broadbent & Poon, 2015; Lee, Lee &
Watson, 2019) that a high level of familiarity with such self-regulatory skills as time
management or effort-regulation, a component of self-instruction, has a positive
relationship with overall academic achievement in the context of online learning
within higher education settings.
This study, however, revealed an even bigger complexity of identified student
respondents’ approaches to learning. First, some respondents claimed to apply
effective learning strategies without realising it (see section 4.2.5), which suggests
both, students’ low levels of awareness of these learning strategies, and their intuitive
trial and error approach. In addition, while some students were unaware of their trial
254 Discussion
and error approach, many others were in fact well aware of it. Research within
educational psychology, especially metamemory, has documented students’ low
awareness levels of learning principles (Soderstrom, Yue & Bjork, 2016) and their
willingness to follow their intuition (Kornell & Bjork, 2007), and it has pointed
towards the negative consequences of such attitudes in terms of preventing or slowing
down development of active and autonomous learning.
Second, it was also found that students reacted mostly positively to the
suggestion of being taught explicitly about learning strategies (section 4.2.8). As
reported by Soderstrom, Yue and Bjork (2016), the benefits of explicitly teaching
learning strategies to undergraduate students have been studied, proven, and evidenced
(McCabe, 2011); thus, such a finding confirms previous research outcomes. However,
as demonstrated, this finding did not stem directly from student respondents’
awareness of their own learning needs. Some respondents perceived the need for
learning about learning strategies as a result of insufficient teaching approaches, rather
than as a result of conscious process of self-evaluation. As explained in section 4.2.8,
some students found the learning and teaching approaches applied in their units
confusing and, as a result, felt that learning about learning strategies might assist them
in learning within this environment.
This, in turn, suggests relatively low levels of self-evaluation, leading to
misconceptions about learning principles and misperceptions (often manifested in the
form of overestimation) of one’s learning effectiveness, resulting in the illusion of
competence (Castel, McCabe & Roediger, 2007; Koriat & Bjork, 2005; Soderstrom,
Yue & Bjork, 2016), which is discussed in the next section.
5.2.1.3 Phase 3 in Zimmerman’s model of self-regulation
With regards to self-evaluation and causal attribution, the two categories constituting
self-reflection in Zimmerman’s model, this study has found that respondents were
misperceiving their learning skills, misjudging their competence in learning, and
overestimating their skills. However, the study also revealed students’ low awareness
levels of their misperceptions, which led them to a false impression of being
sufficiently equipped with learning skills to succeed in their unit/degree (section 4.2.7).
Overall, this indicates the rather low awareness levels of students’ self-reflective skills.
Research indicates that self-reflection on one’s learning is the inference-drawing,
hypothesis-testing and sense-making process that enables learners to draw conclusions
Discussion 255
and strategically plan for the future (Ertmer & Newby, 1996). On the other hand, the
identified low awareness levels do not necessarily signify an absence of self-reflection.
It appears that students were rather confused about the principle of self-reflection,
which may have led to application of trial and error approaches.
For instance, some respondents were aware of using, sometimes intuitively,
trial and error strategies to succeed in their studies. They were also aware of the need
to develop effective learning strategies; yet they continued with a trial and error
approach which, at least to some extent, enabled them to satisfy their learning needs
and progress with their studies. Such practices have already been reported in the
literature (Kornell & Bjork, 2007; Soderstrom, Yue & Bjork, 2016). For instance,
besides students’ preference towards intuitively following learning strategies (Kornell
& Bjork, 2007), it has also been reported that students would first attempt to satisfy
their learning needs (Soderstrom, Yue & Bjork, 2016). Findings of this study (sections
4.1 and 4.2) confirmed research about students’ highly pragmatic practices, such as
prioritising study for assessment, followed by studying for the subjects they consider
the most challenging (Kornell & Bjork, 2007). Furthermore, Karpicke, Butler and
Roediger (2009) have also observed that even if students applied effective learning
strategies, their choice was either intuitive or motivated by pragmatics rather than
resulting from a conscious application of effective learning strategies. The findings in
this study confirmed such observations. Finally, a false sense of knowing/ fluency can
result in overestimations of one’s memory and underestimation of the problem with
forgetting, which may lead to the illusion of competence (Castel, McCabe & Roediger,
2007; Koriat & Bjork, 2005; Soderstrom, Yue & Bjork, 2016).
The section on self-modification of learning practices (4.2.6) demonstrated a
limited capability of respondents to modify their learning practices, focusing on
improvements that did not require in-depth self-reflection and application of more
sophisticated strategies. It is hypothesised that such an approach resulted in an
“illusion of competence”, preventing some students from consciously looking for more
effective learning strategies. The consequences of an illusion of competence are far
reaching, not only from a self-regulation perspective, but also for a self-directed one.
The findings related to the latter are discussed below.
256 Discussion
5.2.2 Respondents’ low levels of awareness of self-directed learning skills
The concept of self-directed learning (see section 2.9.1) can be understood as a
combination of external, environmental, and internal, personal characteristics that
enable individuals to formulate their own learning needs, set learning goals, and select
learning tasks to reach these learning goals (Eva & Regehr, 2005; van Meeuwen et al.,
2013). Self-direction is the process of becoming, of taking on an identity (Eva &
Regehr, 2005; van Meeuwen et al., 2013) and as such requires learners’ proactive
approaches and experience to enable them to take control strategically of their own
learning.
This study found that respondents’ networked learning practices, as presented
in section 4.2, exhibited traits characteristic of novice learners, who tend to be reactive
rather than proactive in their approaches to learning (Cleary & Zimmerman, 2000;
Zimmerman, 2002) (see section 2.9.3 for a definition of a novice learner). For instance,
in case of both units under investigation, many connections offered by learning
networks were made, predominantly, for pragmatic reasons, with the aim of co-
constructing knowledge (i.e., making sense of the content) only for the purpose of
consuming it later to satisfy the need of successfully passing the assessment (sections
4.1 and 4.2). Furthermore, the interactions, the vehicles of networked learning
practices, occurred predominantly within formal learning networks and were not really
successful in promoting the process of feeding the knowledge back to the network
(sections 4.1 and 4.2). For instance, respondents’ assistance with learning and
(infrequent) contributions to the online resources resulted from mateship, or
pragmatics (expectation of returning a service) rather than from conscious application
of self-directed learning strategies with the aim of achieving one’s learning goals.
In general, phases of self-directed learning build on self-regulated learning
skills that are considered to be antecedents to self-direction (Boyer et al., 2013)
(section 2.9.1). Much of the discussion in section 5.2.1 is therefore also relevant to the
current section. There is, however, an important condition. For self-direction to be
enabled, the self-regulatory antecedents need to be in the process of development (i.e.,
state of becoming, taking on an identity), and self-direction needs to capitalise on the
levels of achievement of antecedents. While self-regulation necessitates
operationalising (e.g., planning, setting up goals), self-direction requires a certain
Discussion 257
maturity from the learner that can only be achieved with time, through learning and
experience.
Section 4.1.1, which described respondents’ frames of reference, provided
evidence that the respondents were indeed in the process of building their identity as
university students; however, the process was slow and focused on cognitive
engagement (i.e., learning the content) rather than on social engagement (i.e.,
becoming a member of student networks).
This study has identified a number factors that might have contributed to
slowing down the process, all having diversity as a common denominator. The
description of students’ frames of reference (section 4.1.1) revealed a diversity of: 1)
motivations to undertake a university degree; 2) age; and 3) work experience. In
addition, the demographic data (section 3.2.3, Tables 3.3, 3.4, 3.5 and 3.6) revealed
diversity in students’: 1) degree of enrolment (with many students enrolled in double
degrees); and 2) previous learning experiences in the context of higher education.
According to the university databases, the highest education level of many students
enrolled in both units indicated graduate incomplete. This suggests that many
respondents, despite being enrolled in first semester, first year units of study, already
had previous experiences of learning in the context of higher education. Therefore, the
functional context, through appropriate learning design making salient affordances for
active and autonomous learning, in theory should have stimulated the development of
their self-regulatory and, indirectly, self-directed learning skills. However, as
demonstrated, such development was stimulated only partially, with a focus on taking
up learning affordances to satisfy a need, and with learning strategies applied on a trial
and error basis.
This finding has consequences for deciding on discipline content and designing
learning experiences. University students are facing a double challenge of developing,
in parallel, their content knowledge of the discipline alongside the learning skills that
would enable them to become active learners. The learning design of the units, through
the functional context, attempted to stimulate the learning and development of both
content knowledge and learning skills. This study has demonstrated that, at least with
regards to the learning skills, the proposed approach was not (fully) successful. This
raises questions about respondents’ readiness to study at the university level, including
preparedness in terms of their learning skills.
258 Discussion
There is a growing body of evidence suggesting that direct educational
interventions involving explicit teaching of learning strategies might be beneficial to
students (McCabe, 2011; Soderstrom, Yue & Bjork, 2016). Considering the above and
building on research on active learning (section 2.9), two questions are to be asked:
should self-regulation and self-direction, or more broadly, learning skills, be explicitly
taught to: 1) learners, especially those in the novice stage; and 2) educators, especially
academic teaching staff who not only design learning experiences, but also embody
the idea of experts to whom respondents turn for assistance in the first instance (section
5.1).
Section 4.2.8 provided evidence that the suggestion of explicitly teaching
effective learning strategies was well received by student respondents who could see
the benefits of such direct guidance. The academic teaching staff’s reactions were
more nuanced, depending on their individual perceptions of learning affordances
provided within the functional context they created and on students’ practices. This
question is discussed in more detail in section 5.3.
This section has discussed the impact of identified low levels of both self-
regulated and self-directed learning skills on the development of learners’ active
learning skills. The direct impact of low levels of these skills on learners’ ownership
of their PLEs was also discussed. The concept of connectivism (Duke, Harper &
Johnston, 2013) suggests that human learning is a process that occurs within PLEs
through a set of connections that allow access to the experience of others who are
members of the network, both within the environment and as part of knowledge
production. The consequences of demonstrated low levels of both suggest that
ownership of PLEs (hence learning networks) and the quality of interactions within
learning networks are important, especially from the perspective of learners’ self-
regulation, self-direction, and their development of active learning. Self-directed
learning supports the process of connecting and interacting, and both processes lead to
the development of one’s actionable knowledge whereby knowing how to access
distributed knowledge is at least as important as acquiring new knowledge. This, in
turn, is a stepping stone towards epistemic fluency (Markauskaite & Goodyear, 2017a;
2017b; 2018a; 2018b), the knowing how to transgress the disciplinary boundaries to
co-create new knowledge.
However, from the perspective of this study, to achieve epistemic fluency, an
important stepping stone is learning design, which can be seen as part of the functional
Discussion 259
context that is designed and operationalised by academics. Their perceptions,
including their perceptions of students’ perceptions, influence the final design and
enactment of design principles. The next section discusses the findings related to
academic staff perceptions of student perceptions.
5.3 WHAT ARE ACADEMIC STAFF PARTICIPANTS’ PERCEPTIONS OF
STUDENTS’ NETWORKED LEARNING PRACTICES WITHIN THE
PRESUPPOSED LEARNING NETWORKS?
In response to the third research question, there were two main findings, both related
to the academic teaching staff’s low levels of awareness. First, it was demonstrated
that academics were only moderately familiar with three aspects related to their
students’ academic identity and practices: 1) their students’ frames of reference
(section 4.3.1); 2) the uptake of learning affordances (section 4.3.2); and 3) students’
networked learning practices (section 4.3.3). The second finding revealed that
academics were often basing their opinions, and consequently, acting on assumptions,
rather than evidence. This study has suggested that this resulted from low levels of
familiarity with/ awareness of the pedagogical underpinnings and the methodology of
applying selected pedagogical approaches (section 4.3.4).
It appears that the identified low levels of awareness originated in academic
teaching staff’s frames of reference, especially in their perceptions stemming from
their position of academically successful content experts. The concept of frame of
reference (section 2.2.1) points towards the reciprocal (Fettes, 2003) relationship
between a social agents’ cognitive development and the environment. This study
emphasised the third element of the relationship – perceptions (of the environment),
as they ensue directly from a social agent’s features. To better understand the origin of
such perceptions, this study has proposed using Bioecological Model of Human
Development (Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) to explain their
nature. In this way, the definition of the frames of reference, as used in this study,
could be fine-tuned so that the social agents’ features encapsulate the three ecological
systems: micro-, meso-, and exo-system. Thus, the fact of being academically
successful content experts impacts on the ways social agents perceive all elements
(systems) of the environment and act upon it.
260 Discussion
As the frame of reference is often not fully realised by social agents, its
influence and impact may be difficult to control, which in turn may lead to interference
of perceptions (e.g. student vs discipline expert perceptions), and/ or to assumptions.
The next two sections discuss findings related to the two above-identified phenomena.
5.3.1 The interference of students and academic teaching staff’s perceptions
As explained in section 4.3, the interference of perceptions is described in terms of a
superposition of perceptions that occur at the same time, some of which converge,
some diverge, and combined, they create a third perception. It appears that a pattern of
interference of perceptions can be identified, depending on the interplay between
social agents’ frames of reference (including ecological system), the functional
contexts, the types of learning networks and the nature of learning affordances (need
vs opportunity). That is, it appears that the social agents’ perceptions, occurring within
formal learning networks that provide affordances to satisfy learning needs and enable
interactions with experts, do converge. The more open and unstructured a learning
environment becomes, the more perceptions diverge.
This finding, particularly the fact of raising academic teaching staff’s
awareness of the phenomenon of interference of perceptions, may have implications
for learning design, especially for designing tasks that use self-regulated and self-
directed learning, and for the application of connected learning ideas in the curriculum.
Social agents, such as learners and educators taking part in the learning process,
need to realise the nature (i.e., the type of learning network) and complexity of the
learning environment (i.e., what are constituting parts of the learning networks) as well
as their own frames of references. When designing a learning task, the awareness of
converging perceptions can assist with designing tasks that would satisfy students’
learning needs. On the other hand, the awareness of diverging perceptions would
inform the design of tasks that would satisfy learners’ opportunities for learning. Such
awareness would help with scoping the design of learning tasks related to the
development of the above-discussed self-regulated and self-directed learning skills.
Moreover, it would also ease academic teaching team frustrations, often manifested by
opinions such as “students are only doing what is assessed”, which occur when learners
only take up those affordances that would satisfy their perceived learning needs.
On the other hand, the awareness of interference of perceptions would also
assist with designing for connected learning, as it would provide valuable information
Discussion 261
about which learning affordances are perceived and taken up by students and academic
teaching staff. Such a knowledge would also encourage epistemic practices
(Markauskaite & Goodyear, 2017a; 2017b; 2018a; 2018b) as this would provide
opportunities for learners to realise the potential of their learning networks.
In summary, the finding related to the interference of perceptions has made
salient the importance of being aware of one’s own perceptions. This study has already
demonstrated the important influence exercised by different ecological systems on
students’ perceptions (sections 5.1 and 5.2). The same mechanism applies to academic
staff. The systems’ impact affects firstly the process of constructing learning networks
(i.e. perceiving certain affordances, not perceiving or ignoring others), secondly the
ways in which learning networks are used productively to enable (or not) quality
interactions, and thirdly the process of making certain learning affordances more
salient then others (e.g. by creating the need and an opportunity for learning).
The low level of awareness of one’s perceptions may also lead to assumptions
discussed in the next section.
5.3.2 Academic teaching staff’s assumptions and the need of raising teaching
awareness
This study has demonstrated that many academic teaching staff were basing their
decisions regarding content and pedagogical approaches on assumptions, rather than
facts (section 4.3.4.1). This finding further supported the findings emphasising the
importance of one’s frame of reference and its influence on perception. These findings
pointed to the differences in frames of reference between academic teaching team
members and students as the main, and often not fully realised, reason(s) for
assumptions being made. Furthermore, assumptions can lead to inappropriate
expectations, as demonstrated in this study in the form of the example of expectations
of academic teaching staff members with regards to the preparedness level of
commencing students, especially in terms of their content knowledge and learning
strategies (section 4.3.4.1).
This study has also demonstrated that such assumptions partially resulted from
academic teaching teams’ frames of reference10, including lack of familiarity with the
10 Many academics were either of international background or were not registered teachers enabling them to teach in Queensland high schools.
262 Discussion
Australian/ Queensland high school curriculum, which was not expected from them
by the Faculty anyway. However, it appears that academic teaching team members, as
content experts, sometimes had difficulties in realising that their expertise in content
knowledge overlapped, to varying degrees, with their pedagogical knowledge.
However, the impact of content expertise on the process of becoming a skilful teacher
is not new and has been described in the literature on expertise (section 2.11.3). As
Bransford et al. (2000) observed:
[C]ontent experts are usually not skilful teachers, because they tend
to “forget” about the difficulties they had to overcome in becoming
experts. (p. 47)
This study has provided evidence that, despite careful design of the functional
context (see section 1.8 for detailed description), the assumptions of academic teaching
staff who were not involved in the process of design negatively impacted on teaching
practice and the ways in which learning affordances were made salient to students. It
appears that the academic teaching team members not included in the process did not
take ownership of the pedagogical principles underpinning the design (section 4.3.4.2).
This finding raises questions about the pedagogical preparedness and teacher
preparation of academic teaching staff members11. While academic teachers should no
doubt have pedagogical foundations, the state of being a (content) expert, achieved
through a time-consuming process of becoming a (content) expert, has a very strong
impact on one’s frame of reference, and thus perceptions. The state of being a content
expert positions academics in a unique situation where sharing knowledge, providing
advice, and explaining things are highly sought after by learners. This has been
illustrated by empirical evidence of student respondents openly preferring to learn
from/ with experts (section 4.1).
From the perspective of learning design, it appears that within the context of
learning networks and networked learning of distributed knowledge, the roles of social
agents taking part in educational practice are changing. With increased importance of
learner self-regulation, self-direction, and autonomy within personalised learning
11 This refers to all types of academic teaching staff, including tutors. For recent discussion of the importance of teacher preparation especially of tutors, see Kahu and Picton, 2019.
Discussion 263
environments, the academic teaching team member’s role is being transformed. The
role of an academic teacher is no longer just a guide on the side (King, 1993), as it
seems to shift from “conveyor of knowledge to designer of learning” (Konnerup,
Ryberg & Sørensen, 2018, p. 333), and many researchers are arguing in favor of
perceiving academic teachers as ‘designers’ (Laurillard, 2012; Goodyear, 2015).
However, as this study has demonstrated, within the increasing complexity of learning
networks, learners need powerful strategies to connect, interact, and de-, re-, and co-
construct knowledge. In short, they need experts in content knowledge and in learning
and teaching approaches. As discussed in section 2.9.3, becoming an expert is a
lengthy process that results in deep cognitive changes to one’s perception of the
surrounding reality. The process depends on the one’s cognitive stage (see Piaget’s
theory in Ginsbourg & Opper, 2016) and level of “approximation of reality”
(Kirschner, 2009, p. 146). Such a deep change in cognition influences one’s perception
and makes it difficult for an expert to “cognitively go back” and see the problem as a
novice (Bransford et al., 2000). Thus, becoming an expert in two domains of
knowledge (e.g., mathematics and learning design) needs specific education, training,
and experience in both domains. Thus, based on the findings that have demonstrated
an increased need for learning from/with diverse experts, and building on research
within cognition (becoming an expert), this study calls for a reconsideration of the role
of the academic teacher as an epistemically fluent expert in the (learning) network,
surrounded by other experts such as industry partners or education specialists (e.g.,
learning designers) and working in partnerships with them. In this context, the concept
of partnership, especially with students (students asn partners) is understood as “a
collaborative, reciprocal process through which all participants have the opportunity
to contribute equally, although not necessarily in the same ways, to curricular or
pedagogical conceptualization, decision-making implementation, investigation, or
analysis” 12. For a most recent discussion on the concept of partnerships and its
application in the context of higher education, see Cook-Sather, Bahti, & Ntem, A,
(2019), and Quinn, L. (2019). (Cook-Sather, Bovill & Felten, 2014, pp. 6-7).
These experts, working in partnerships, assist students in de-, re-, and co-
constructing distributed knowledge and thus in becoming epistemically fluent active
12 For a most recent discussion on the concept of partnerships and its application in the context of higher education, see Cook-Sather, Bahti, & Ntem, A, (2019), and Quinn, L. (2019).
264 Discussion
learners (Markauskaite & Goodyear, 2017a; 2018a; 2018b). However, the expert in
the network needs to have well-developed epistemic competencies (Goodyear &
Carvalho 2014a; 2014b; 2016), encompassing disciplinary and pedagogical
knowledges, which would enable the expert to design learning tasks that would foster
a learner’s epistemic fluency.
Section 2.1.2 discussed the ontological paradigm underpinning this study,
which has a particular focus on an ecological perspective on human learning. A deep
reflection on all findings of this study has led to the development of an ecological
curriculum and learning design framework that encompasses epistemic domains and
social agents’ systems. This proposed framework is discussed in detail in Chapter 6.
5.4 REFLECTION ON DISCUSSION
This chapter has provided a discussion of findings into the ways formal and informal
learning networks were used and learning practices enacted by undergraduate, first-
year students enrolled in STEM-focused vocational degrees. In addition, the study has
painted a rich picture of academic teaching staff’s perceptions of students’ uptake of
learning affordances offered by learning networks and networked learning practices.
Finally, the study has revealed academic teaching staff’s perceptions of students’
levels of awareness of effective learning strategies and their preparedness levels to
undertake undergraduate studies in a STEM-focused discipline at a particular
Australian university.
This chapter has led to five conclusions. The first conclusion is related to the
theoretical underpinnings of this study and proposes a modified definition of the
concept of affordance. The next three conclusions are related to findings and
discussion of the three research questions, and they are used to develop an argument
for: 1) involving expert partners in the process of curriculum and learning design, 2)
explicitly teaching effective learning strategies to students, and 3) making changes in
enhancing the learning and teaching expertise of academic teaching teams. The last,
and overall conclusion, leads to an argument in favour of implementing the proposed
ecological curriculum and learning design framework, as developed in this study. The
framework itself stems from a reflection on the nature of learning networks.
Discussion 265
As mentioned in section 2.6, Siemens has more recently modified his views on
networks and has emphasised the dominating role of systems over networks. A system
can be seen as an ecology, a closed entity that defines its own components and
determines their interactions, while networks only support the system or the
connections between the components of the system (Siemens, 2019). This study has
attempted to illustrate the dominant role of the system over the network by observing
and describing the relationships between the surrounding environments, the learning
affordances they offered, and the type of the ecological system the constituents of the
learning network belonged to. This might have implications for learning design, as it
points towards the role the broader social structures (systems) might play as
determiners of whether the network (i.e., the connections) would even be created (or
not) and whether networked practices (interactions) would be fostered. For instance,
as this study has demonstrated, professionals (industry partners) did not belong to
neither students’ nor academic teaching staff’s ecological systems. Neither group
perceived the learning affordances, such as an opportunity to learn from a professional
expert, offered by these social agents. Thus, the potential of the learning network was
impoverished as the opportunity to promote epistemic cognition and assist learners in
becoming epistemically fluent was not seized.
The next chapter discusses all five conclusions in more detail.
266 Discussion
Conclusions 267
Conclusions
This chapter presents the conclusions to this study, and discusses potential implications
for curriculum and learning design practice and theory. The chapter then outlines
directions for further research resulting from the study. The closing section reflects on
the contribution made by this study to the body of knowledge about the ways we, as
humans, perceive and act within a complex learning environment.
6.1 CONCLUSIONS
The main objective of creating one’s PLE, including formal and informal learning
networks, is to own the process of learning and to increase one’s (or learners’) agency
(Casquero et al., 2013; 2016; Jones & Issroff, 2005; Orlikowski, 2002; Tolmie &
Boyle, 2000). This requires learners’ awareness of the elements constituting their PLEs
and the agentic attitude manifested in monitoring and effectively (i.e., productively)
participating in the learning processes occurring within their PLEs by interacting,
collaborating, co-creating new knowledge and feeding it back to the network (Downes,
2010a, 2012; Siemens, 2004, 2005a, 2005b; 2006; n.d.).
These processes, however, are not innate to learners and require specific
strategies. To this end, a systematic, research-underpinned and evidence-informed
approach is needed, and this study has proposed such an approach, based on
conclusions resulting from the findings. The first two conclusions discussed in the next
section relate to the research paradigm of the study.
First, based on research on learning networks (Goodyear et al., 2004; Goodyear
& Carvalho, 2014a; 2014b) and above-mentioned connectivism, a modified definition
of the concept of productive learning network and networked learning practices has
been advanced. Considering the distributed nature of networked knowledge, the
importance of returning co-constructed knowledge back to the network seems pivotal.
This is to ensure the connections between the entities sustain the network and energise
it, so it can take on its own dynamics. Therefore, the study proposes the following,
modified definition of productive learning network (see also section 1.4) (changes in
italics):
268 Conclusions
Productive networked learning is understood as the process that
involves the application of an assemblage forming a learning
network (composed of artifacts, people, ideas and practices), as part
of taking action by a learner to complete a learning task. During this
process, the knowledge is de-constructed and re-constructed, and the
learning environment within which the activity takes place is co-
configured. The co-construction of knowledge is understood as the
process of taking distributed knowledge from the network, de- and
co-constructing it, and feeding it back to the network, in the form of
an intellectual contribution (e.g. digital or material artifact, assisting
other learner with learning). As a result, new knowledge is co-
created and re-distributed within the network.
Second, the study has also led to advancing a modified definition of the notion of
affordance in particular, the concepts of affordance and the frame of reference, as
presented in section 2.2.1 (Czaplinski, 2013). This study has revealed an important
difference between a need and an opportunity for learning. It was demonstrated, in
particular in sections 4.1 and 4.2, that student respondents were focused on satisfying
their learning needs in the first instance, while taking advantage of learning
opportunities was secondary. As the identified distinction between a learning need and
a learning opportunity was absent in the definition of the concept, this study suggests
modifying the original definition (Czaplinski, 2013) in the following way (changes in
italics):
Affordance – an occasion to satisfy one’s learning needs, and/or to
take advantage of a learning opportunity, both embedded in the
functional context and triggered by it. This is an occasion to take an
action, which can be perceived or not, taken up or not. Its perception
depends on a social agent’s frame of reference. However, the action
of taking it up is governed by, and occurs within, the functional
context. Any modifications in the frame of reference and/or
functional context influence the perception and the uptake of
affordances.
Conclusions 269
In relation to the concept of frame of reference, this study suggested (section 5.4) to
fine-tune its definition by encapsulating the three ecological systems: micro-, meso-,
and exosystem in the explanation of the social agents’ features. Thus, the modified
definition of the frame of reference is (changes in italics):
Frame of reference – a set of features allowing the perception of
affordances offered by the surrounding environment and influencing
the decision of taking them up or not. An individual’s frame of
reference is influenced by ecological systems that have a different
impact, depending on the type of the system. Frame of reference can
be modified according to the surrounding environment, (changing)
circumstances, and under the influence of new experiences. A social
agent can be more or less aware of the frame of reference and can
control to some extent the variations occurring within it (e.g.
choosing to draw lessons from previous experiences). However, the
frame of reference can also be modified without a social agent
having control over the process.
Conclusion two relates to the first research question. Student respondents were
predominantly using their formal learning networks to satisfy their learning needs
when learning with/ from experts. Their interactions within the formal learning
network were focused on consuming knowledge from the network, which impeded
their ability to co-create new knowledge and to make their learning networks more
productive. This limited use of learning networks is evidence of low ownership levels
of students’ PLEs. To address this issue, this study proposes to involve expert partners
in the curriculum and learning design processes, which offers the potential to increase
learners’ and academic teaching teams’ exposure to diverse knowledges and ways of
knowing (epistemic fluency) (see below for more details). Furthermore, the learning
experiences designed as a result of such a collaboration would include research-
underpinned and evidence-based suggestions for appropriate learning strategies that
would have the potential of developing learners’ agency, in particular, by developing/
enhancing their self-directed and self-regulated learning, and/ or by building effective
learning strategies into task and assessment designs. Finally, this also offers the
270 Conclusions
potential for increasing the use of informal learning networks and their productivity,
by encouraging more diverse epistemic practices.
The next two conclusions ensue from the findings and discussion related to
research questions two and three. As has been demonstrated, learners’ low awareness
levels of effective learning strategies negatively impacted on their learning,
contributing to the above-described vicious circle mechanism, which results in an
illusion of competence. This study has formulated two proposals to address this issue.
First, it is proposed to include explicit instruction of effective learning strategies in
learning design, with special attention paid to the development of self-regulation and
self-direction. Second, it is also proposed to include expert curriculum/ learning
designers/ developers in the learning design process, as members of the learning design
team, to ensure that effective learning strategies are embedded in the learning tasks
and experiences (see below ecological curriculum and learning design framework of
connected epistemic domains for details).
As for research question three, the study identified interference of students and
academics’ perceptions as one of main sources of academic assumptions. This study
suggests involving academic teaching teams in the curriculum and learning design
processes as expert partners. The involvement of academic teaching teams (e.g. tutors)
as expert partners offers the potential for increasing their pedagogical awareness, and
provides opportunities for making interdisciplinary connections, thus enhancing their
epistemic fluency. Furthermore, this would also prepare academic teaching team
members who are not coordinating the units of study (and hence do not have the
responsibility of designing learning experiences) for their roles as academic experts in
the learning design teams.
This study has shown the need to re-conceptualise curriculum and learning
design in order to promote connected learning and teaching approaches that would
improve undergraduate STEM students’ connectivity, increase the potential of
productively using learning networks, and promote their interdisciplinary exposure to
become epistemically fluent, active, lifelong learners. To address this need, this study
proposes a new ecological curriculum and learning design framework, which is
context-specific (i.e., for STEM disciplines). The framework has already been
mentioned in section 5.3.2 and is presented in Figure 6.1.
Based on the Activity-Centred Analysis and Design (ACAD) analytical
framework, developed by Peter Goodyear and Lucila Carvalho (2014a; 2014b; 2016)
Conclusions 271
(section 2.10), and on Bronfenbrenner’s Bioecological Model of Human Development
(Bronfenbrenner, 2005; Bronfenbrenner & Morris, 2006) (section 2.1.2), this
framework also incorporates the concept of epistemic fluency developed by
Markauskaite and Goodyear (2017a; 2017b; 2018a; 2018b). The framework represents
an ecology of epistemic domains and respective systems of social agents who interact
and play different roles in curriculum and learning design processes.
The three epistemic domains (of ideas, design and practice) are interconnected
and support the process of learning and becoming an epistemically fluent, lifelong
learner. Each domain has a specific function, described below:
1. The domain of ideas, also referred to as the domain of epistemic fluency. This
is the system of partners, some of them experts (e.g., industry, university
systems, and high school teachers), some in the process of becoming experts
(e.g., students), and finally some others in the process of achieving expertise in
two disciplines, content, and approaches to learning and teaching (e.g.,
academic teaching teams). Partners provide input, in the form of their
interdisciplinary specific knowledges and experiences, to ensure that epistemic
richness and fluency will be included in the process of curriculum design.
These specific knowledges belong to the conceptual domain of ideas,
represented by the concept of epistemic fluency. The relationship between
domain of ideas and systems of partners is reciprocal, that is, partners share
their ideas that create intellectual potential which, in turn, contributes to/
enriches partners’ knowledges. For instance, the input from partners enables
academic teaching teams to perceive learning affordances that might not be
perceived in the first instance, as they form a part of more subtle informal
learning network. The broadened perception of academic teaching teams may
lead to more interdisciplinary design, by interacting with learning design
teams.
272
Con
clus
ions
Figu
re 6
.1:E
colo
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ricul
um a
nd le
arni
ng d
esig
n fr
amew
ork
of c
onne
cted
epi
stem
ic d
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Conclusions 273
As a result, this would make learning affordances salient that hitherto would
not necessarily have been perceived, either by academic teaching teams or by
learning design teams. In consequence, some of these affordances can be designed as learning needs
which, as this study has demonstrated, are the first to be satisfied by learners.
Moreover, the specific knowledge of academic teaching teams would be
enriched by ideas that form the domain of epistemic fluency.
In this way, favourable conditions to foster epistemic fluency have been
created.
2. The domain of design, situated in the centre of the framework, this domain
illustrates the learning design phase or epistemic design, where two types of
experts, representing their specific knowledges, work in collaboration to design
learning experiences for students. The nature and level of the experts varies,
depending on their primary discipline knowledge (i.e., content knowledge
expertise) and secondary expertise. For this study, the expert knowledges are
situated on a spectrum from Engineering, Science, to curriculum/ learning
design, to specific knowledge of methodologies for teaching STEM
disciplines. By working in collaboration, both types of experts share their
specific, interdisciplinary knowledges, which then results in a state of
overlapping expertise. The relationship between the system of experts and the
conceptual domain of epistemic design is reciprocal. That is, the overlapping
expertise of learning design teams contributes to the intellectual potential of
epistemic design, which, in turn, enriches overlapping expertise itself. In short,
the partnership between different experts is aimed at ensuring the right
problems are being solved in the right way. To guarantee the epistemic richness
and fluency of the learning experiences, the design is also informed by inputs
and advice provided by partners. As a result of such collaboration, learning
design teams become aware of diverse knowledges and ways of knowing
distributed in the learning networks and can design epistemic practices.
3. The domain of epistemic practice. This domain incorporates a modified ACAD
framework, a system of novice learners (i.e., novice when it comes to content
knowledge at the beginning of the semester) and the conceptual domain of
274 Conclusions
practice. Being a novice relates primarily to the content knowledge and, as this
study has demonstrated, to their awareness of powerful learning strategies. The
learners, in the course of their unit enrolment, through designed tasks, are
encouraged to take advantage of the learning affordances embedded in both
formal and informal learning networks, which then results in emergent activity
of networked practice. Learning becomes an emerging activity of co-
constructing knowledge using distributed networked knowledges, and learners
complete designed tasks using learning networks supported by social practices
and the use of settings.
Social practice is an opportunity for collaborative learning offered by
social agents who belong to formal and informal learning networks. As the
design was, at least partially, influenced by experts as partners, the learning
need of collaborating with peers and experts has been created and students have
the opportunity to become aware of diverse experts in the network representing
diverse knowledges.
Practice of settings is an opportunity to use tools, artifacts, places, and
spaces of formal and informal learning networks. Similar to social practice,
where the design is influenced by experts as partners, the practice of settings is
influenced by a system of partners, and designed, predominantly in formal
learning networks, by a system of experts.
As with previous domains, the conceptual domain of epistemic practice
is also in a reciprocal relationship with the system. Emergent activity, the
practical application of the designed task, informs the ways the elements of the
domain are being used. This information enriches the intellectual potential of
the epistemic practice, that is, how the design was applied, and which learning
affordances were perceived and taken up (or not).
The proposed ecological curriculum and learning design framework, if applied,
provides ways to design in opportunities for learners to practice diverse knowledges
and ways of knowing to cross the boundaries between knowledges and to start taking
control of the process of co-configuration and co-creation of knowledge. With time,
epistemically fluent, active, lifelong learners will become experts and may contribute
their knowledge through partnerships with learning design teams.
Conclusions 275
However, as observed above, the proposed model is contextualised to address
the specific needs of STEM undergraduate students and academic teaching teams. Yet,
the model offers potential to be implemented and tested in other disciplines, as it
concerns a broader problem of the role curriculum/ learning designers/ developers
should play in contemporary higher education. In addition, the model also addresses
the problem of the breadth and depth of academic teaching teams’ preparedness levels
to teach in the context of higher education institutions. As this is only one of a range
of possible implications stemming from the study, the next section discusses other
identified implications.
6.2 IMPLICATIONS
The study also addressed five research needs formulated in section 1.5. First, the study
has provided strong evidence of student respondents’ pragmatic attitudes and strategic
approaches when perceiving affordances for learning, and in taking them up (or not).
Furthermore, the study has also presented an evidence-based interpretation of students’
attitudes and approaches. The interpretation has suggested complex inter-
dependencies between students’ frames of reference, their sense of belonging to one
of the ecological systems, and the surrounding environment. The revealed correlations
between student respondents’ frames of reference, elements of the learning networks
and the ecological systems have the potential to inform learning design effectively, as
they are strongly rooted in evidence-based data analysis of concrete examples. For
instance, the study pointed towards the important role that expertise and being an
expert played for student respondents. This resulted in a description of the teaching
role (of academics, more advanced peers, and members of informal learning networks)
in terms of being an expert in the network. This change in perspective on the role of a
teaching academic also aligns with the second need addressed in this study, namely
the need to educate epistemically fluent, active, lifelong learners.
Second, the proposed ecological curriculum and learning design framework of
connected epistemic domains pointed towards the need of involving diverse experts in
the curriculum and learning design processes. The study has provided a strong
argument in favour of including diverse experts, all contributing to the intellectual
potential of epistemic fluency, as partners in the framework. That is, first, the study
has demonstrated the importance of creating a need for learning (in the form of
learning affordances) that is salient to students. Next, the study has pointed towards
276 Conclusions
the difference between the need for learning and opportunities for learning. The
revealed difference suggests that, to enable epistemic fluency, learning design teams
should consider turning some opportunities for learning into learning needs. This
applies especially to opportunities that are crucial for becoming epistemically fluent
(e.g., learning from experts, members of informal learning networks), as these
opportunities require more effort from learners to be perceived and taken up.
Transforming them into needs might facilitate their perception and uptake. Finally, the
study has also demonstrated the need to involve different experts, such as academic
content experts, industry partners (e.g., professionals), and curriculum/ learning
designers/ developers (experts in learning design and pedagogy). Their collaboration,
based on the principle of partnership, offers the potential to shift from an idiosyncratic
approach to learning design, focused on individual components (i.e., content
knowledge learning supported by skills acquisition), to a holistic or amalgamated
approach (i.e., content knowledge embedded in skills acquisition).
The next research need addressed by the study is the need for more research on
ways to promote active learning. The study calls for including the teaching of effective
learning strategies, or learning how to learn, within the formal learning environment
as well as external learning networks, as part of curriculum and learning design.
Responding to this call could satisfy two objectives: the student respondents’
awareness of effective learning strategies and the ways of applying them for active
learning would be raised. In addition, academic teaching teams’ awareness of such
strategies would be raised as well. Moreover, including learning strategies as part of
the curriculum and learning design would provide academic teaching teams with an
opportunity for professional development that would enhance their pedagogical
awareness and knowledge of learning design.
Fourth, the study has addressed the need for contextualised and situated
classroom research on STEM education. As there is a real need to address the identified
interference of perceptions which, as demonstrated in the study, effectively impacts
on both learning design and academic teaching teams’ pedagogical practices, knowing
your students (commencing and current) becomes one of the keys for successful
student engagement. In practical terms, this study presents an evidence-based
argument in favour of using expert advice and targeted information to provide
academics with specific information about their students. For instance, STEM-focused
disciplines have been discussing the problem of commencing students’ preparedness
Conclusions 277
levels in mathematics for quite some time. The proposed ecological curriculum and
learning design framework of connected epistemic domains presents a practical
approach to this problem by suggesting university and high school teachers be included
as expert partners in curriculum and learning design. Including the above-mentioned
partners in the discussion offers real potential to tackle effectively the problem of
disconnection between prospective students’ actual competence levels in mathematics,
institutionally assumed knowledge, and the levels expected by academic teaching
teams, as well as the consequences emanating from such a disconnection.
The fifth and final need addressed by this study is the need for more
contextualised research on curriculum and learning design for STEM disciplines in
higher education. The study culminates in a proposed ecological curriculum and
learning design framework of connected epistemic domains. Thorough evidence
emerged from the study that supports a call to include diverse experts who bring
specific knowledges as part of curriculum and learning design, for re-thinking the
composition of learning design teams and the ways they collaborate, and finally, for
re-conceptualising the task design, to enable epistemic practices to be created for
student learning. The proposed framework offers real potential to be applied in
authentic settings and to inform evidence-based curriculum and learning design for
networked learning.
6.3 LIMITATIONS
This study has seven limitations that are systematically discussed below.
The first and most important limitation relates to the use of self-reported gains
(Bowman, 2011), or self-reported data (Gonyea, 2005; Hoskin, 2012) from
questionnaires, as one of the main data sources (complemented by focus groups). The
self-reported gains can bring important benefits, such as: 1) greater bandwidth of data;
2) practicality and economy of data collection; and 3) consideration for contextual
variance (e.g., institution, program) (Gonyea, 2005, p. 74). Nevertheless, this type of
data collection can also be subject to different forms of bias (Tourangeau, Rips &
Rasinski, 2000; Gonyea, 2005; Bowman, 2011; Hoskin, 2012), such as 1) dishonest
responses; 2) social desirability; and 3) halo error (Gonyea 2005; Bowman 2011). This
study has addressed all of these by using a number of strategies.
To limit the potential bias for dishonest responses, the questionnaire, modelled
on questions used in previously published research, predominantly sought factual
278 Conclusions
information by asking about respondents’ practices and overall circumstances in which
they were learning (e.g., study time and place). The focus group questions were
formulated following the preliminary findings from the questionnaire, and they were
thus targeted, enquired about specific issues, and presented fewer chances for
respondent bias. The clear wording of the questions also addressed potential problems
with understanding and interpretation. The literature indicates that the accuracy, and
thus the reliability, of responses to self-reported questionnaires increases with the
clarity of their wording (Gonyea, 2005; Noble & Sawyer, 1988). The questions were
also pre-tested by a group of volunteering students and academics not involved in the
project, to ensure questions’ clarity and lack of ambiguity and to limit potential issues
with the interpretation of the questions.
As the study investigated respondents’ perceptions, some attitudinal questions
(Tourangeau, Rips & Rasinski, 2000; Gonyea, 2005), based on personal beliefs or
perceptions, were also included to provide respondents with an opportunity to use
introspection (Gonyea, 2005; Bowman, 2011; Hoskin, 2012). Literature indicates that
the ability to introspect might pose problems for some respondents (Gonyea, 2005;
Bowman, 2011; Hoskin, 2012) and the questionnaire was therefore constructed in a
way to encourage introspection. The structure of the questionnaire aligned with a
“four-part response process” (Gonyea, 2005, p. 77): comprehension, retrieval,
judgement and response (Tourangeau, Rips & Rasinski, 2000; Gonyea, 2005). That is,
the attitudinal questions (e.g., “You feel that you belong to the university community”,
or “You think or feel that you have appropriate learning skills to succeed in this unit?”)
were placed towards the end of each of the three parts of the questionnaire (i.e.,
investigating learning networks, networked learning practices, and collecting self-
reported background information), as they posed more cognitive demand on
respondents.
The second type of bias, social desirability (Bowman 2011; Hoskin 2012;
Gonyea, 2005), was also addressed through several strategies. The questionnaire,
which in itself already limits the potential for social desirability bias (Bowman 2011;
Gonyea, 2005), was anonymous and there was no pre-existing relationship between
the research participants and the researcher. This meant a limited need for the use of
impression management strategies (Gonyea, 2005, p. 82) and thus a limited potential
for the phenomenon of “pleasing the researcher”. Furthermore, the questionnaire used
a variety of response scales, including dichotomous questions (yes/no), non-numerical
Conclusions 279
Likert-scale responses, or soft response categories (Gonyea, 2005, p. 79), (e.g., always,
most of the time, sometimes, occasionally, never). The richness of scales was aimed
at increasing objectivity and reliability. To limit the problem of “common quantifiable
understanding” (Gonyea, 2005, p. 79) of the meaning of the words used in soft
response categories, focus groups (also anonymous) provided opportunities to clarify
any ambiguity related to understanding (and interpreting) the proposed non-numerical
scales.
Lastly, the halo error (Bowman 2011; Gonyea, 2005), or the “tendency to give
consistent evaluations across a set of specific items based on a general perception of
the subject” (Gonyea, 2005, p. 83), was addressed by using numerical rating questions
(e.g., from 1 to 7). The potential problem of restrictive rating scales (Hoskin, 2012)
(i.e. too tight or too broad) was addressed by offering a well-balanced variety of scales,
ranging from 1-7 to 1-14, depending on question. This provided the respondents with
the opportunity for more nuanced (and thus potentially less biased) responses within a
balanced scale. Moreover, the numerical data were analysed using specialised
statistical software and a sophisticated methodology to establish the ranking of
responses. As explained in section 3.4, although median was used as the leading
measure, the reported data also considered a Mann -Whitney test, and an interquartile
range (25-75%) to rank the responses.
Finally, collating the questionnaires in person provided the researcher with the
opportunity to control the sample (Hoskin, 2012) and to ensure each questionnaire was
filled in by only one respondent.
The second limitation regards the limited scope of the study. Data were
collected from only two selected units at a single university, and opportunistic
sampling procedures were used. This could have negatively impacted on the research
quality as it limited the number and diversity of respondents and research contexts,
which could potentially have skewed the data towards one type of respondent.
However, appropriate steps were taken, as outlined in section 3.4, to counterbalance
the risks and ensure trustworthiness of the research as much as possible. In addition,
conducting contextualised research actually contributes to the body of knowledge and
responds to the need of evidence-informed, contextualised and situated studies on
learning design to promote the process of becoming epistemically fluent, lifelong
active learners (section 1.6).
280 Conclusions
Third limitation ensuing from the previous limitation, is the data collection
technique used. Data were collected using only paper-based questionnaires, with
research participants who attended the targeted and timetabled face-to-face classes. To
mitigate this limitation, in semester 2, 2017, in parallel with the focus groups, an online
version of the questionnaire was also released. This was a means of triangulating data,
aimed at assuring the quality of the research. As expected, there were few responses
to the online version provided by 17 students out of 158 across both units. The response
rate was very low, reaching only 10% of the entire targeted group, and did not reveal
any additional, unexpected information or new findings, compared to the ones already
identified through the paper-based method. Considering the above, the online data
were perused to confirm the observed patterns and trends from the paper-based
questionnaire but were not included in the data analysis.
Fourth limitation relates to the study timelines. The data collection was
completed over six months (only), focusing on one year’s group of respondents (“local
conditions” effect described by Cronbach (1975)), which might not consider the
potential changes in the cohort’s profile over time. Again, these limitations were
addressed in section 3.4 on the trustworthiness of the study, which explained in detail
how the six guidelines ensuring research reliability, validity and objectivity were
observed.
Fifth limitation is that the study was completed by one researcher (although
working under supervision of other experienced researchers). The researcher’s frame
of reference, encompassing her professional and personal identities, certainly impacted
on the qualitative data analysis and interpretation of findings. That is, the researcher’s
professional background in learning design might have impacted on the interpretation
of the data, especially with regards to the role expert curriculum/ learning designers/
developers would play in the proposed ecological curriculum and learning design
framework of connected epistemic domains. Again, steps (described in section 3.4)
were taken to minimise personal bias by using mixed methods research and to avoid
(or minimise) conscious bias.
Sixth limitation relates to the fact that, as explained in section 3.4, the
conclusions drawn from case studies are often difficult to generalise, due to the
continuous change of the conditions within which the studies are conducted
(Cronbach, 1975). However, the well-founded design of the study provided grounds
for fuzzy generalisations (Bassey, 1999) to be made that led to development of the
Conclusions 281
ecological curriculum and learning design framework of connected epistemic
domains. However, following Yin (2013), to confirm the findings and ensuing
conclusions, replications of the study are needed (see section 6.4 for discussion of
further research).
The seventh and last limitation is related to the fact that the researcher was not
directly involved in task design or teaching. Thus, many aspects of the learning design
process were beyond reach (e.g., selecting content, planning activity, developing
resources). On the other hand, this allowed for a more objective lens, which was
focused on the educational, pedagogical aspects of the units under investigation.
Moreover, the researcher was working under supervision of the academic who
originally designed and taught the units, and hence there was opportunity to directly
discuss research findings and ensure that the necessary information (clarifications) on
the content and pedagogy were provided. Such objectivity should be seen as an
element supporting the quality of findings, especially in light of research which calls
for more multidisciplinary teams to work on complex, wicked problems (Markauskaite
& Goodyear, 2017a).
6.4 FURTHER RESEARCH
The results of this study have demonstrated the necessity for further research into the
above-mentioned research needs. This includes further research on learning networks
and networked learning to investigate the revealed patterns forming learning networks,
identified relationships and their consequences for learning design.
Furthermore, the proposed ecological curriculum and learning design
framework of connected epistemic domains itself needs further research to investigate
its effectiveness, especially in the context of STEM-focused disciplines. That is, this
research has pointed towards the need for further investigations into the ways the
proposed framework could be used as the foundation for the systematic investigation
of model(s) of collaboration between social agents within the framework, and the
process of designing learning tasks that would address the challenge of assisting
students in becoming epistemically fluent, lifelong learners. This is not a new idea, as
the ecological metaphor has been applied to educational research, especially higher
education, for some time (Ellis & Goodyear, 2010; Ellis & Goodyear, 2019).
Reflecting on the role an ecological approach has to play in assisting contemporary
282 Conclusions
learners in becoming active, epistemologically fluent, lifelong learners, Ellis and
Goodyear (2019) observe:
One of the core purposes of HE is to help people become more
capable, autonomous lifelong learners. A vital but rather neglected
aspect of being an autonomous learner is knowing how to create for
oneself, and one’s colleagues, a well-furnished learning
environment. In this sense, it helps if lifelong learners become their
own educational ecologists, knowledgeable about how to create and
sustain their own congenial sites for learning and enquiry, or in other
words, able to engineer appropriate ‘epistemic niches’ (Sterelny,
2012; Markauskaite & Goodyear, 2017) (p. 5).
The investigation into applications of the ecological curriculum and learning design
framework of connected epistemic domains, as developed in this study, fully
subscribes to the above-mentioned research directions and offers the potential of
situating future investigations into higher education settings, and in the context of
STEM-focused disciplines. It should also be noted that further research would provide
some research-based (as opposed to research-informed) examples of applications
based on the results of this study.
Furthermore, the ecological nature of the research has attracted attention to the
crucial problem of “knowing one’s student” (see sections 4.3 and 5.3). Current work
in the field of learning analytics
2019; Shibani, Knight & Buckingham Shum, 2019) might shed some light and provide
more scalable and applicable solutions to the challenge of meaningfully using an
apparent abundance of student/ learner information to support the process of learners
taking ownership of their PLEs.
Some of this suggested research has already been initiated by the researcher/
author of this doctoral dissertation. For instance, the research on potential
collaboration model(s) that would encompass collaboration within one system (e.g. the
system of partners) and between the systems (e.g. system of partners and the system
of experts) has been initiated. The initial work on this question stems directly from this
study (Winter, Czaplinski, Apps & Mallet, 2018; Czaplinski, Mallet & Huijser, 2019;
Czaplinski, Turner, Helmstedt, Corry & Mallet, 2019) and proposes microcultures
Conclusions 283
(Heinrich, 2017; Roxå & Mårtensson, 2015; Roxå, Mårtensson & Alveteg, 2011) as a
potential model for applied partnerships.
Next, research directions identified by this thesis point towards more in-depth
investigation of the design of learning tasks that would enable epistemic fluency,
students’ self-regulation and self-direction. The potential research would include such
questions as “How to design learning tasks that enable epistemic fluency and
practices?” or “How to raise students’ awareness about opportunities for epistemic
fluency and practices, especially those offered within informal learning networks?”
The study also pointed towards the need for further research on the ways of
raising learners and academic teachers’ awareness about powerful and effective
learning strategies. Potential research questions would investigate students’ and
academic staff perceptions of explicitly teaching learning strategies. Furthermore, the
problem of explicitly teaching learning strategies, combined with the above-mentioned
problem of operationalising collaboration within individual systems (e.g. system of
experts), leads to further research on teachers as designers (Goodyear, 2015). Such
research could investigate potential responses to such questions as: “What is the type
(e.g. content, pedagogical), breadth and depth of the expertise of academics currently
designing learning experiences for their students?”, or “What type of expertise, in
terms of its breadth and depth, should be brought in by a Curriculum/ Learning
Developer/ Designer to ensure a richness (and relevance) of the overlapping expertise
of learning design team members?”
6.5 CLOSING REFLECTION
The ecological view on human cognition emphasises the importance of the interplay
between perception and action. We are living in a networked reality, where the
complexity and density of networks increases exponentially, due to unprecedented
technological developments. To function within such a complex system, one needs to
develop the ability to perceive affordances, estimate their benefits and to act upon them
in a well-thought through, strategic way. Writing from the ecological perspective on
complex systems and reflecting on the relationship between perception and action,
Péter Érdi (2008), author of Complexity explained, observes:
Actions are determined by the goals and not by the trajectories of
movement that form them. Perception does not give information on the
284 Conclusions
present happening only, but tells also what is going to happen soon.
Cognition has a role in extending the time-frame of prediction.
Perception and action are inter-dependent. Actions need perception to
guide them but actions also participate in the process of perception (p.
295).
From the perspective of this study, this could be complemented by a suggestion
that perception gives information not only about the present and the future. It also
informs about the past, as it is founded on previous experiences constituting one’s
frame of reference. Thus, to become a lifelong, epistemically fluent, active learner, one
first needs to become a self-aware, reflective person, and a self-regulated and self-
directed learner. Nowadays, this becoming takes place within a connected, digitally-
rich network, which means knowing one’s learning needs (or, more broadly, the needs
to become), how to perceive an opportunity for learning, how to consider its benefits
and act upon it. This study is an attempt to attract readers’ attention to the importance
of assisting learners in the process of perceiving, considering and wisely acting on
their perceptions in the process of becoming lifelong, epistemically fluent active
learners.
App
endi
ces
285
App
endi
ces
A. Q
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A.1
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286
App
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App
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287
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of I
NTEN
SITY
(fr
om th
e m
ost a
dvan
tage
take
n to
the
leas
t ad
vant
age
take
n):
Prac
tice
the
new
cont
ent p
rese
nted
at t
he le
ctur
e
W
ork
colla
bora
tivel
y w
ith o
ther
stud
ents
W
ork
colla
bora
tivel
y w
ith h
elp
from
teac
hers
, if n
eede
d
M
eet f
ace-
to-fa
ce w
ith th
e te
ache
rs
Mee
t fac
e-to
-face
with
oth
er st
uden
ts
Prac
tice
for a
sses
smen
t
288
App
endi
ces
Prac
tice
lear
ning
skill
s (w
ork
orga
nisa
tion,
tim
e-m
anag
emen
t, fo
cus,
etc.
)
O
ther
……
……
……
…..
If yo
u m
issed
a W
ORK
SHO
P, h
ow d
id
you
catc
h up
with
lear
ning
?
Plea
se e
xpla
in:
You
atte
nded
wee
kly
COM
PUTE
R LA
BS
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
If
you
atte
nded
the
wee
kly
com
pute
r lab
s (ev
en o
ccas
iona
lly),
plea
se e
xpla
in w
hy:
If yo
u di
d no
t att
end
the
wee
kly
com
pute
r lab
s, pl
ease
exp
lain
why
:
Wha
t wer
e th
e op
port
uniti
es fo
r le
arni
ng o
f the
wee
kly
COM
PUTE
R LA
BS?
Opp
ortu
nity
for l
earn
ing.
Pl
ease
chec
k as
man
y bo
xes a
s app
ropr
iate
.
Pl
ease
rank
them
in o
rder
of I
MPO
RTAN
CE:
Prac
tice
the
new
cont
ent p
rese
nted
at t
he le
ctur
e (a
nd th
e w
orks
hop)
W
ork
indi
vidu
ally
, with
hel
p fr
om th
e te
ache
rs, i
f nee
ded
Wor
k in
divi
dual
ly w
ith h
elp
from
oth
er st
uden
ts, i
f nee
ded
Mee
t fac
e-to
-face
with
the
teac
hers
Mee
t fac
e-to
-face
with
oth
er st
uden
ts
Acce
ss sp
ecia
lised
softw
are
Prac
tice
for a
sses
smen
t
Pr
actic
e le
arni
ng sk
ills (
wor
k or
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
Oth
er (p
leas
e sp
ecify
): So
far,
do y
ou fe
el y
ou to
ok a
dvan
tage
of
thes
e op
port
uniti
es?
Opp
ortu
nity
for l
earn
ing.
Pl
ease
chec
k as
man
y bo
xes a
s app
ropr
iate
.
Pl
ease
rank
them
in o
rder
of I
NTEN
SITY
(fro
m th
e m
ost a
dvan
tage
take
n to
the
leas
t adv
anta
ge ta
ken)
:
Prac
tice
the
new
cont
ent p
rese
nted
at t
he le
ctur
e (a
nd th
e w
orks
hop)
App
endi
ces
289
Wor
k in
divi
dual
ly, w
ith h
elp
from
the
teac
hers
, if n
eede
d
W
ork
indi
vidu
ally
with
hel
p fro
m o
ther
stud
ents
, if n
eede
d
M
eet f
ace-
to-fa
ce w
ith th
e te
ache
rs
Mee
t fac
e-to
-face
with
oth
er st
uden
ts
Acce
ss sp
ecia
lised
softw
are
Prac
tice
for a
sses
smen
t
Pr
actic
e le
arni
ng sk
ills (
wor
k or
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
Oth
er …
……
……
..
If
you
miss
ed a
CO
MPU
TER
LAB,
how
did
yo
u ca
tch
up w
ith le
arni
ng?
Plea
se e
xpla
in:
Yo
u co
uld
see
clear
conn
ectio
ns
betw
een
the
lect
ures
, wor
ksho
ps, t
he
com
pute
r lab
s and
lear
ning
mat
eria
l pr
ovid
ed to
you
(e.g
. via
Bla
ckbo
ard)
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
If
you
did
not s
ee th
e co
nnec
tion,
ple
ase
expl
ain
why
:
Your
lear
ning
out
side
cont
act h
ours
(fac
e-to
-face
clas
sroo
m a
ctiv
ities
) PE
OPL
E W
hen
lear
ning
out
side
the
cont
act
hour
s (cla
ssro
om a
ctiv
ities
), W
HO
assis
ts y
ou in
you
r lea
rnin
g?
Plea
se ch
eck
as m
any
boxe
s as a
ppro
pria
te:
Plea
se ra
nk th
em in
ord
er o
f IM
PORT
ANCE
:
QUT
teac
hing
staf
f
Q
UT su
ppor
t sta
ff (e
.g. S
TIM
ulat
e, Li
brar
y)
Fello
w st
uden
ts e
nrol
led
in th
is un
it
Stud
ents
from
oth
er U
nis
Teac
hing
staf
f fro
m o
ther
Uni
s
Prof
essio
nals
Priv
ate
tuto
rs
Fa
mily
mem
ber
(not
pro
fess
iona
l or f
rom
the
Uni)
Oth
er (p
leas
e sp
ecify
):
290
App
endi
ces
PEO
PLE
WHO
do
you
cont
act t
he M
OST
FREQ
UENT
LY?
Plea
se ra
nk th
em in
ord
er o
f FRE
QUE
NCY
(the
mos
t of
ten
cont
acte
d pe
rson
to th
e le
ast o
ften
cont
acte
d pe
rson
).
WHO
do
you
cont
act F
IRST
(the
n se
cond
, thi
rd, e
tc.)
whe
n as
king
for
assis
tanc
e?
Plea
se ra
nk th
em in
SEQ
UENT
IAL
orde
r.
So fa
r, do
you
feel
you
TOO
K AD
VANT
AGE
of a
ssist
ance
pro
vide
d by
th
ese
PEO
PLE?
Pl
ease
rank
them
in o
rder
of I
NTEN
SITY
(fr
om th
e m
ost a
dvan
tage
take
n to
the
leas
t adv
anta
ge ta
ken)
. Q
UT te
achi
ng st
aff
QUT
supp
ort s
taff
(e.g
. STI
Mul
ate,
Lib
rary
)
Fello
w st
uden
ts e
nrol
led
in th
is un
it
St
uden
ts fr
om o
ther
Uni
s
Te
achi
ng st
aff f
rom
oth
er U
nis
Pr
ofes
siona
ls
Priv
ate
tuto
rs
Fam
ily m
embe
r (n
ot p
rofe
ssio
nal o
r fro
m th
e Un
i)
Oth
er …
……
……
…..
W
HO, i
n yo
ur o
pini
on, a
ssist
s you
the
best
in y
our l
earn
ing
outs
ide
the
cont
act h
ours
?
WHY
do
you
say
that
this
pers
on a
ssist
s you
the
best
? HO
W d
oes t
his p
erso
n as
sist y
ou?
Plea
se e
xpla
in:
Ar
e yo
u as
sistin
g so
meo
ne in
his/
her
lear
ning
?
If ye
s, w
hy?
If no
, why
?
DEVI
CES
Whe
n le
arni
ng o
utsid
e th
e co
ntac
t ho
urs (
class
room
act
iviti
es),
WHI
CH
DEVI
CE a
ssist
you
with
you
r lea
rnin
g?
Plea
se ch
eck
as m
any
boxe
s as a
ppro
pria
te:
Plea
se ra
nk th
em in
ord
er o
f IM
PORT
ANCE
:
Yo
ur la
ptop
Your
tabl
et
Your
mob
ile p
hone
Yo
u do
not
hav
e an
y de
vice
App
endi
ces
291
Oth
er (p
leas
e sp
ecify
):
DEVI
CE
W
HICH
DEV
ICE
do y
ou u
se th
e M
OST
FRE
QUE
NTLY
for y
our l
earn
ing
outs
ide
the
cont
act
hour
s?
Plea
se ra
nk th
em in
ord
er o
f FRE
QUE
NCY
(the
mos
t ofte
n us
ed d
evice
to th
e le
ast o
ften
used
dev
ice).
So fa
r, do
you
feel
you
TOO
K AD
VANT
AGE
of
the
poss
ibili
ties f
or le
arni
ng p
rovi
ded
by th
ese
DEVI
CES?
Pl
ease
rank
them
in o
rder
of I
NTEN
SITY
(fro
m
the
mos
t adv
anta
ge ta
ken
to th
e le
ast
adva
ntag
e ta
ken)
. Yo
ur d
eskt
op co
mpu
ter
Yo
ur la
ptop
Your
tabl
et
Your
mob
ile p
hone
Oth
er …
……
……
……
..
WHI
CH D
EVIC
E, in
you
r opi
nion
, ass
ists
you
the
best
in y
our l
earn
ing
outs
ide
the
cont
act h
ours
?
WHY
do
you
say
that
this
devi
ce a
ssist
s you
the
best
? HO
W d
oes t
his d
evice
ass
ist y
ou?
Plea
se e
xpla
in:
ON
LINE
RES
OUR
CES
Whe
n le
arni
ng o
utsid
e th
e co
ntac
t ho
urs (
class
room
act
iviti
es),
WHI
CH
ONL
INE
RESO
URCE
ass
ist y
ou w
ith
your
lear
ning
?
Plea
se ch
eck
as m
any
boxe
s as a
ppro
pria
te:
Plea
se ra
nk th
em in
ord
er o
f IM
PORT
ANCE
:
QUT
Bla
ckbo
ard
site
QUT
Libr
ary
site
In
tern
et w
ebsit
e (g
ener
al)
Spec
ialis
ed In
tern
et w
ebsit
es
(e
.g. j
ourn
als,
othe
r uni
s)
This
unit’
s Fac
eboo
k (if
app
licab
le)
Yo
ur p
erso
nal F
aceb
ook
(if a
pplic
able
)
Socia
l med
ia (e
.g. T
witt
er)
Spec
ialis
ed b
logs
Sp
ecia
lised
chat
room
s
292
App
endi
ces
You
Tube
Khan
Aca
dem
y
Prof
essio
nal n
etw
ork
com
mun
ities
(e
.g. O
pen
Sour
ce)
MO
OCs
(e.g
. Cou
rser
a, Ly
nda.
com
)
Mob
ile p
hone
app
s
O
ther
(ple
ase
spec
ify):
ONL
INE
RESO
URCE
W
HICH
onl
ine
reso
urce
do
you
cons
ult t
he M
OST
FRE
QUE
NTLY
? Pl
ease
rank
them
in o
rder
of F
REQ
UENC
Y (th
e m
ost o
ften
cons
ulte
d on
line
reso
urce
to th
e le
ast o
ften
cons
ulte
d on
line
reso
urce
).
WHI
CH o
nlin
e re
sour
ce d
o yo
u co
nsul
t FIR
ST (t
hen
seco
nd, t
hird
, etc
.) w
hen
lear
ning
?
Plea
se ra
nk th
em in
SE
QUE
NTIA
L ord
er.
So fa
r, do
you
feel
you
TOO
K AD
VANT
AGE
of p
oten
tial f
or
lear
ning
offe
red
by th
ese
reso
urce
s?
Plea
se ra
nk th
em in
ord
er o
f IN
TENS
ITY
(from
the
mos
t ad
vant
age
take
n to
the
leas
t ad
vant
age
take
n).
QUT
Bla
ckbo
ard
site
Q
UT Li
brar
y sit
e
In
tern
et w
ebsit
e (g
ener
al)
Sp
ecia
lised
Inte
rnet
web
sites
(e.g
. jou
rnal
s, ot
her u
nis)
This
unit’
s Fac
eboo
k (if
app
licab
le)
Your
per
sona
l Fac
eboo
k (if
app
licab
le)
Socia
l med
ia (e
.g. T
witt
er)
Sp
ecia
lised
blo
gs
Spec
ialis
ed ch
at ro
oms
Yo
u Tu
be
Khan
Aca
dem
y
Pr
ofes
siona
l net
wor
k co
mm
uniti
es
(e.g
. Ope
n So
urce
)
MO
OCs
(e.g
. Cou
rser
a, Ly
nda.
com
)
App
endi
ces
293
Mob
ile p
hone
app
s
Oth
er …
……
……
……
WHI
CH O
NLIN
E RE
SOUR
CE, i
n yo
ur
opin
ion,
ass
ists y
ou th
e be
st in
you
r le
arni
ng o
utsid
e th
e co
ntac
t hou
rs?
WHY
do
you
say
that
this
onlin
e re
sour
ce a
ssist
s you
the
best
? HO
W d
oes t
his o
nlin
e re
sour
ce a
ssist
you
? Pl
ease
exp
lain
:
Are
you
cont
ribut
ing
to th
e on
line
reso
urce
s?
For e
xam
ple,
by
keep
ing/
writ
ing
for a
bl
og, T
wee
ting
abou
t scie
ntifi
c top
ics,
taki
ng p
art i
n a
prof
essio
nal d
iscus
sion
foru
m, e
tc.?
If ye
s, w
hy?
If
no, w
hy?
W
hen
lear
ning
out
side
the
cont
act
hour
s (cla
ssro
om a
ctiv
ities
), do
you
us
e pa
per-b
ased
text
book
s?
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
Pl
ease
exp
lain
why
II.In
this
sect
ion,
I w
ould
like
to fi
nd o
ut a
bout
you
r net
wor
ked
lear
ning
hab
its. H
ow d
o yo
u st
udy
in th
is un
it?
In
this
unit,
you
usu
ally
stud
y (p
leas
e ch
eck
as m
any
boxe
s as a
ppro
pria
te)
Al
one
With
you
r frie
nd/ c
lass
mat
e
In
a g
roup
of m
inim
um 3
Oth
er (p
leas
e sp
ecify
): At
hom
e
On-
cam
pus
On
publ
ic tr
ansp
ort
Som
ewhe
re e
lse (p
leas
e sp
ecify
): In
this
unit,
you
pla
n yo
ur st
udyi
ng in
ad
vanc
e St
rong
ly
Mod
erat
ely
A
gree
M
oder
atel
y
Stro
ngly
a
gree
a
gree
disa
gree
disa
gree
In th
is un
it, d
o yo
u st
udy
for a
lect
ure?
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
I
onl
y
stu
dy fo
r
as
sess
men
t
294
App
endi
ces
If yo
u do
stud
y fo
r a le
ctur
e, H
OW d
o yo
u st
udy?
Do y
ou st
udy
befo
re o
r afte
r the
lect
ure,
alo
ne o
r with
som
eone
else
, usin
g on
line
reso
urce
s or n
ot, a
t hom
e or
on
cam
pus?
If/ w
hen
you
need
an
assis
tanc
e, w
ho d
o yo
u co
ntac
t firs
t, at
wha
t poi
nt in
tim
e (b
efor
e st
udyi
ng, d
urin
g or
afte
r stu
dyin
g)?
Plea
se d
escr
ibe:
In th
is un
it, d
o yo
u st
udy
for a
w
orks
hop/
com
pute
r lab
?
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
I
onl
y
stu
dy fo
r
as
sess
men
t
If yo
u do
stud
y fo
r a w
orks
hop/
co
mpu
ter l
ab, H
OW
do
you
stud
y?
Do y
ou st
udy
befo
re o
r afte
r the
lect
ure,
alo
ne o
r with
som
eone
else
, usin
g on
line
reso
urce
s or n
ot, a
t hom
e or
on
cam
pus?
If/
whe
n yo
u ne
ed a
n as
sista
nce,
who
do
you
cont
act f
irst,
at w
hat p
oint
in ti
me
(bef
ore
stud
ying
, dur
ing
or a
fter s
tudy
ing)
? Pl
ease
des
crib
e:
In
this
unit,
HOW
MUC
H TI
ME
per
wee
k do
you
spen
d on
stud
ying
ou
tsid
e cla
ssro
om h
ours
?
Plea
se sp
ecify
:
In y
our o
pini
on, H
OW
MUC
H TI
ME
you
SHO
ULD
spen
d on
stud
ying
for t
his
unit
outs
ide
class
room
hou
rs?
Plea
se sp
ecify
:
In th
is un
it, w
hat d
o yo
u do
with
the
lear
ning
mat
eria
l pro
vide
d to
you
? Pl
ease
tick
one
or m
ore
boxe
s:
You
keep
it o
nlin
e in
its o
rigin
al d
estin
atio
n
You
dow
nloa
d it
to y
our d
evice
You
mak
e ha
nd-w
ritte
n no
tes a
nd st
ore
them
in a
file
(not
eboo
k)
Yo
u m
ake
note
s ele
ctro
nica
lly a
nd st
ore
them
in a
dig
ital f
ile/ n
oteb
ook
You
do n
ot o
rgan
ise y
our l
earn
ing
mat
eria
l
You
orga
nise
you
r mat
eria
l diff
eren
tly (p
leas
e de
scrib
e):
In
this
unit,
you
mak
e yo
ur o
wn
lear
ning
mat
eria
l Al
way
s
Mos
t of t
he ti
me
Som
etim
es
O
ccas
iona
lly
Nev
er
App
endi
ces
295
In y
our o
pini
on, L
EARN
ING
AT Y
OUR
O
WN
PACE
usin
g a
blen
d of
in-c
lass
ac
tiviti
es, o
nlin
e re
sour
ces a
nd y
our
pers
onal
lear
ning
net
wor
ks is
an
effe
ctiv
e st
rate
gy fo
r you
r stu
dyin
g.
Stro
ngly
M
oder
atel
y
Agr
ee
Mod
erat
ely
St
rong
ly
agr
ee
agr
ee
d
isagr
ee
d
isagr
ee
Pl
ease
exp
lain
why
:
Base
d on
you
r exp
erie
nce,
as y
ou
wen
t, di
d yo
u m
odify
the
way
you
w
ere
lear
ning
in th
is un
it du
ring
the
sem
este
r?
Yes
No
If
yes,
WHA
T di
d yo
u m
odify
? If
no, W
HY n
ot?
III.
Now
, I w
ould
like
to le
arn
a bi
t mor
e ab
out y
ou
You
are
Fe
mal
e
Mal
e
Dom
estic
stud
ent
Inte
rnat
iona
l stu
dent
……
……
……
……
……
. yea
rs o
ld
the
first
in th
e fa
mily
(i.e
. gra
nd-p
aren
ts, p
aren
ts, s
iblin
gs) t
o at
tend
uni
vers
ity
Yes
No
Whi
ch le
vel o
f Mat
hem
atics
hav
e yo
u co
mpl
eted
to y
ear 1
2?
Qld
Mat
hs A
or e
quiv
alen
t (“g
ener
al m
aths
”)
Q
LD M
aths
B o
r equ
ival
ent (
inclu
ding
func
tions
and
som
e ca
lculu
s)
Q
ld M
aths
C o
r equ
ival
ent (
high
est l
evel
)
Oth
er (p
leas
e sp
ecify
):
You
stud
y Fu
ll-tim
e
Part
-tim
e
Wha
t deg
ree
do y
ou st
udy?
You
wor
k Pa
rt-ti
me
Ful
l-tim
e
I do
not
wor
k
If yo
u w
ork,
yo
ur w
ork
is re
late
d to
you
r deg
ree
you
are
stud
ying
. Ye
s
No
296
App
endi
ces
You
feel
that
you
bel
ong
to th
e un
iver
sity
com
mun
ity
Stro
ngly
M
oder
atel
y
Agr
ee
Mod
erat
ely
St
rong
ly
agr
ee
agr
ee
d
isagr
ee
d
isagr
ee
You
tend
to m
ix w
ith o
ther
QUT
st
uden
ts:
ON
CAM
PUS
Stro
ngly
M
oder
atel
y
Agr
ee
Mod
erat
ely
St
rong
ly
agr
ee
agr
ee
d
isagr
ee
d
isagr
ee
ONL
INE
Stro
ngly
M
oder
atel
y
Agr
ee
Mod
erat
ely
St
rong
ly
agr
ee
agr
ee
d
isagr
ee
d
isagr
ee
Yo
u ge
t inv
olve
d in
ext
ra-c
urric
ular
ac
tiviti
es (e
.g. s
tude
nt cl
ubs)
Ye
s
No
Do
you
thin
k yo
u ha
ve a
ppro
pria
te
LEAR
NING
SKI
LLS
to su
ccee
d in
this
unit?
Yes
No
N
ot su
re
If no
, whi
ch LE
ARNI
NG S
KILL
S w
ould
you
nee
d to
dev
elop
(ple
ase
spec
ify):
IV.
Fina
l com
men
ts y
ou m
ight
hav
e
App
endi
ces
297
A.2
Staf
f ver
sion
Dear
Tea
cher
,
Than
k yo
u fo
r ass
istin
g m
e in
my
rese
arch
on
stud
ents
’ lea
rnin
g ne
twor
ks (w
ho a
nd w
hat d
o th
ey co
nsul
t whe
n le
arni
ng) a
nd o
n th
eir l
earn
ing
netw
ork
habi
ts (h
ow o
ften,
in
wha
t ord
er, w
hy d
o th
ey co
nsul
t the
peo
ple,
dev
ices a
nd re
sour
ces t
hey
are
usin
g, e
tc.).
I h
ighl
y va
lue
your
resp
onse
s and
than
k yo
u ve
ry m
uch
for a
ll yo
ur h
elp,
Iwon
a
I.In
this
part
, I a
m in
tere
sted
in y
our s
tude
nts’
lear
ning
net
wor
ks. W
ho a
nd w
hat a
ssist
s the
m w
ith le
arni
ng?
Th
eir l
earn
ing
durin
g co
ntac
t hou
rs (f
ace-
to-fa
ce cl
assr
oom
act
iviti
es)
Appr
oxim
atel
y w
hat %
of y
our s
tude
nts
atte
nded
wee
kly
LE
CTUR
ES
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
…
...%
……
%
…
… %
…
… %
…
… %
W
hat w
as re
ason
they
att
ende
d th
e w
eekl
y le
ctur
es (e
ven
occa
siona
lly)?
W
hat w
as th
e re
ason
they
did
not
att
end
the
wee
kly
lect
ures
? W
hat w
ere
the
oppo
rtun
ities
for
lear
ning
offe
red
by th
e w
eekl
y LE
CTUR
ES?
Opp
ortu
nity
for l
earn
ing.
Pl
ease
chec
k as
man
y bo
xes a
s app
ropr
iate
.
Pl
ease
rank
them
in o
rder
of I
MPO
RTAN
CE:
Prov
ide
new
cont
ent
Mee
t fac
e-to
-face
with
the
lect
urer
Le
arn
dire
ctly
from
the
lect
urer
M
eet f
ace-
to-fa
ce w
ith o
ther
stud
ents
Le
arn
dire
ctly
from
oth
er st
uden
ts
Ask
ques
tions
/ see
k cla
rifica
tions
dire
ctly
from
the
lect
urer
As
k qu
estio
ns/ s
eek
clarif
icatio
ns d
irect
ly fr
om o
ther
stud
ents
298
App
endi
ces
Lear
n ab
out a
sses
smen
t in
this
unit
Oth
er (p
leas
e sp
ecify
):
Appr
oxim
atel
y w
hat %
of y
our s
tude
nts
wat
ched
LECT
URE
reco
rdin
gs o
n Bl
ackb
oard
site
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
I do
not k
now
…
...%
……
%
…
… %
…
… %
…
… %
……
%
Wha
t was
the
reas
on fo
r wat
chin
g th
e le
ctur
e re
cord
ings
(eve
n oc
casio
nally
)?
Wha
t was
the
reas
on fo
r not
wat
chin
g th
e le
ctur
e re
cord
ings
? Ap
prox
imat
ely
wha
t % o
f you
r stu
dent
s at
tend
ed w
eekl
y
WOR
KSHO
PS
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
I do
not k
now
…
...%
……
%
…
… %
…
… %
…
… %
……
%
Wha
t was
the
reas
on fo
r att
endi
ng th
e w
eekl
y w
orks
hops
(eve
n oc
casio
nally
)?
Wha
t was
the
reas
on fo
r not
att
endi
ng th
e w
eekl
y w
orks
hops
?
Wha
t wer
e th
e op
port
uniti
es fo
r le
arni
ng o
ffere
d by
the
wee
kly
WO
RKSH
OPS
?
Opp
ortu
nity
for l
earn
ing.
Pl
ease
chec
k as
man
y bo
xes a
s app
ropr
iate
.
Pl
ease
rank
them
in o
rder
of I
MPO
RTAN
CE:
Prac
tice
the
new
cont
ent p
rese
nted
at t
he le
ctur
e
W
ork
colla
bora
tivel
y w
ith o
ther
stud
ents
W
ork
colla
bora
tivel
y w
ith h
elp
from
teac
hers
, if n
eede
d
M
eet f
ace-
to-fa
ce w
ith th
e te
ache
rs
Mee
t fac
e-to
-face
with
oth
er st
uden
ts
Prac
tice
for a
sses
smen
t
Pr
actic
e le
arni
ng sk
ills (
wor
k or
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
Oth
er (p
leas
e sp
ecify
):
If yo
ur st
uden
t miss
ed a
WO
RKSH
OP,
ho
w d
id s/
he ca
tch
up w
ith le
arni
ng?
An
y su
spici
ons?
App
endi
ces
299
Appr
oxim
atel
y w
hat %
of y
our s
tude
nts
atte
nded
wee
kly
COM
PUTE
R LA
BS
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
I do
not k
now
…
...%
……
%
…
… %
…
… %
…
… %
……
%
Wha
t was
the
reas
on fo
r att
endi
ng th
e w
eekl
y co
mpu
ter l
abs (
even
occ
asio
nally
)?
Wha
t was
the
reas
on fo
r not
att
endi
ng th
e w
eekl
y co
mpu
ter l
abs?
W
hat w
ere
the
oppo
rtun
ities
for
lear
ning
offe
red
by th
e w
eekl
y CO
MPU
TER
LABS
?
Opp
ortu
nity
for l
earn
ing.
Pl
ease
chec
k as
man
y bo
xes a
s app
ropr
iate
.
Pl
ease
rank
them
in o
rder
of I
MPO
RTAN
CE:
Prac
tice
the
new
cont
ent p
rese
nted
at t
he le
ctur
e (a
nd th
e w
orks
hop)
W
ork
indi
vidu
ally
, with
hel
p fr
om th
e te
ache
rs, i
f nee
ded
Wor
k in
divi
dual
ly w
ith h
elp
from
oth
er st
uden
ts, i
f nee
ded
Mee
t fac
e-to
-face
with
the
teac
hers
Mee
t fac
e-to
-face
with
oth
er st
uden
ts
Acce
ss sp
ecia
lised
softw
are
Prac
tice
for a
sses
smen
t
Pr
actic
e le
arni
ng sk
ills (
wor
k or
gani
satio
n, ti
me-
man
agem
ent,
focu
s, et
c.)
Oth
er (p
leas
e sp
ecify
): If
your
stud
ent m
issed
a C
OM
PUTE
R LA
B, h
ow d
id s/
he ca
tch
up w
ith
lear
ning
?
Any
susp
icion
s?
Appr
oxim
atel
y w
hat %
of y
our s
tude
nts
coul
d se
e cle
ar co
nnec
tions
bet
wee
n th
e le
ctur
es, w
orks
hops
, the
com
pute
r lab
s an
d le
arni
ng m
ater
ial p
rovi
ded
to th
em
(e.g
. via
Bla
ckbo
ard)
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
I do
not k
now
…
...%
……
%
…
… %
…
… %
…
… %
……
%
If, in
you
r opi
nion
, the
y di
d no
t see
the
conn
ectio
n, p
leas
e ex
plai
n w
hy:
Your
lear
ning
out
side
cont
act h
ours
(fac
e-to
-face
clas
sroo
m a
ctiv
ities
) PE
OPL
E W
hen
lear
ning
out
side
the
cont
act
hour
s (cla
ssro
om a
ctiv
ities
), W
HO
assis
ts y
our s
tude
nts i
n th
eir l
earn
ing?
Plea
se ch
eck
as m
any
boxe
s as a
ppro
pria
te:
Plea
se ra
nk th
em in
ord
er o
f IM
PORT
ANCE
:
QUT
teac
hing
staf
f
300
App
endi
ces
QUT
supp
ort s
taff
(e.g
. STI
Mul
ate,
Libr
ary)
Fe
llow
stud
ents
enr
olle
d in
this
unit
St
uden
ts fr
om o
ther
Uni
s
Te
achi
ng st
aff f
rom
oth
er U
nis
Pr
ofes
siona
ls
Pr
ivat
e tu
tors
Fam
ily m
embe
r (n
ot p
rofe
ssio
nal o
r fro
m th
e Un
i)
Oth
er (p
leas
e sp
ecify
):
WHO
, in
your
opi
nion
, ass
ists y
ou th
e be
st in
you
r lea
rnin
g ou
tsid
e th
e co
ntac
t hou
rs?
WHY
do
you
say
that
this
pers
on a
ssist
s you
the
best
? HO
W d
oes t
his p
erso
n as
sist y
ou?
Plea
se e
xpla
in:
In
you
r opi
nion
, you
r stu
dent
s are
ta
king
adv
anta
ge o
f the
opp
ortu
nitie
s fo
r lea
rnin
g fo
rm a
noth
er p
erso
n.
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
T
hey
only
I do
not k
now
stu
dy fo
r
as
sess
men
t
Plea
se g
ive
reas
on fo
r you
r opi
nion
. How
coul
d th
ey im
prov
e?
DEVI
CES
Whe
n le
arni
ng o
utsid
e th
e co
ntac
t ho
urs (
class
room
act
iviti
es),
WHI
CH
DEVI
CE a
ssist
you
r stu
dent
s with
thei
r le
arni
ng?
Plea
se ch
eck
as m
any
boxe
s as a
ppro
pria
te:
Plea
se ra
nk th
em in
ord
er o
f IM
PORT
ANCE
:
Desk
top
com
pute
r
Lapt
op
Ta
blet
M
obile
pho
ne
They
do
not h
ave
any
devi
ce
Oth
er (p
leas
e sp
ecify
): In
you
r opi
nion
, you
r stu
dent
s are
ta
king
adv
anta
ge o
f the
dev
ices t
hat
assis
t the
m w
ith th
eir l
earn
ing.
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
T
hey
only
I do
not k
now
stu
dy fo
r
as
sess
men
t
Plea
se g
ive
reas
on fo
r you
r opi
nion
. How
coul
d th
ey im
prov
e?
App
endi
ces
301
ON
LINE
RES
OUR
CES
Whe
n le
arni
ng o
utsid
e th
e co
ntac
t ho
urs (
class
room
act
iviti
es),
WHI
CH
ONL
INE
RESO
URCE
ass
ist y
our
stud
ents
with
thei
r lea
rnin
g?
Plea
se ch
eck
as m
any
boxe
s as a
ppro
pria
te:
Plea
se ra
nk th
em in
ord
er o
f IM
PORT
ANCE
:
QUT
Bla
ckbo
ard
site
QUT
Libr
ary
site
In
tern
et w
ebsit
e (g
ener
al)
Spec
ialis
ed In
tern
et w
ebsit
es
(e
.g. j
ourn
als,
othe
r uni
s)
This
unit’
s Fac
eboo
k (if
app
licab
le)
Th
eir p
erso
nal F
aceb
ook
(if a
pplic
able
)
Socia
l med
ia (e
.g. T
witt
er)
Spec
ialis
ed b
logs
Sp
ecia
lised
chat
room
s
Yo
u Tu
be
Kh
an A
cade
my
Pr
ofes
siona
l net
wor
k co
mm
uniti
es
(e.g
. Ope
n So
urce
)
MO
OCs
(e.g
. Cou
rser
a, Ly
nda.
com
)
Mob
ile p
hone
app
s
O
ther
(ple
ase
spec
ify):
In y
our o
pini
on, y
our s
tude
nts a
re
taki
ng a
dvan
tage
of t
he o
nlin
e re
sour
ces t
hat a
ssist
them
with
thei
r le
arni
ng.
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
T
hey
only
I do
not k
now
stu
dy fo
r
as
sess
men
t
Plea
se g
ive
reas
on fo
r you
r opi
nion
. How
coul
d th
ey im
prov
e?
II.
In th
is se
ctio
n, I
wou
ld li
ke to
find
out
abo
ut y
our s
tude
nts’
netw
orke
d le
arni
ng h
abits
. How
do
they
stud
y in
this
unit?
In th
is un
it, y
our s
tude
nts p
lan
thei
r st
udyi
ng in
adv
ance
St
rong
ly
Mod
erat
ely
A
gree
M
oder
atel
y
Stro
ngly
I
do
not k
now
a
gree
a
gree
disa
gree
disa
gree
302
App
endi
ces
Wha
t is t
he re
ason
for y
our o
pini
on?
In th
is un
it, y
our s
tude
nts s
tudy
for a
le
ctur
e?
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
T
hey
only
I do
not k
now
stu
dy fo
r
as
sess
men
t
Wha
t is t
he re
ason
for y
our o
pini
on?
In
this
unit,
you
r stu
dent
s stu
dy fo
r a
wor
ksho
p/ co
mpu
ter l
ab?
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
T
hey
only
I do
not k
now
stu
dy fo
r
as
sess
men
t
Plea
se g
ive
reas
on fo
r you
r opi
nion
. In
this
unit,
HO
W M
UCH
TIM
E pe
r w
eek
do y
our s
tude
nts s
pend
on
stud
ying
out
side
class
room
hou
rs?
Plea
se g
uess
: W
hat i
s the
reas
on fo
r you
r opi
nion
?
In y
our o
pini
on, H
OW
MUC
H TI
ME
your
stud
ents
SHO
ULD
spen
d on
st
udyi
ng fo
r thi
s uni
t out
side
class
room
hou
rs?
Plea
se sp
ecify
: W
hat i
s the
reas
on fo
r you
r opi
nion
?
In y
our o
pini
on, i
n th
is un
it yo
ur
stud
ents
are
taki
ng a
dvan
tage
of
lear
ning
opp
ortu
nitie
s cre
ated
by
the
blen
d of
in-c
lass
act
iviti
es, o
nlin
e re
sour
ces a
nd th
eir p
erso
nal l
earn
ing
netw
orks
.
Alw
ays
M
ost o
f the
tim
e
So
met
imes
Occ
asio
nally
N
ever
T
hey
only
I do
not k
now
stu
dy fo
r
as
sess
men
t
Plea
se g
ive
reas
on fo
r you
r opi
nion
.
III.
Now
, I w
ould
like
to le
arn
a bi
t abo
ut y
ou
You
are
Un
it Co
ordi
nato
r
Tut
or
Dem
onst
rato
r
Ap
prox
imat
ely
how
man
y ye
ars o
f te
achi
ng e
xper
ienc
e ha
ve y
ou g
ot?
App
endi
ces
303
How
big
is th
e st
uden
t coh
ort y
ou
teac
h/ h
ave
cont
act w
ith in
this
unit?
Do y
ou th
ink
you
have
app
ropr
iate
LE
ARNI
NG S
KILL
S to
succ
eed
in th
is un
it?
Yes
No
N
ot su
re
Plea
se g
ive
reas
on fo
r you
r opi
nion
. If
no, w
hich
LEAR
NING
SKI
LLS
wou
ld y
ou n
eed
to d
evel
op (p
leas
e sp
ecify
): Do
you
thin
k yo
ur st
uden
ts a
re w
ell
enga
ged
with
: TH
EIR
LEAR
NING
Ye
s
No
Not
sure
Pl
ease
giv
e re
ason
for y
our o
pini
on. H
ow to
impr
ove
thei
r eng
agem
ent?
TH
E UN
IVER
SITY
Ye
s
No
Not
sure
Pl
ease
giv
e re
ason
for y
our o
pini
on. H
ow to
impr
ove
thei
r eng
agem
ent?
IV.
Fina
l com
men
ts y
ou m
ight
hav
e:
304 Appendices
B. FOCUS GROUP QUESTIONS
B.1 Student version
Appendices 305
B.2 Staff version
306 Appendices
References 307
References
Alammary, A. & Karaali, K. (2016). Flipped education: transitioning to the home
school environment. Cogent Education, 3(1224607), 1-9. http://dx.doi.org-
/10.1080/2331186X.2016.1224607
Alammary, A., Carbone, A. & Sheard, J. (2017). Curriculum transformation using a
blended learning design toolkit. In R.G. Walker & S. B. Bedford (Eds.),
HERDSA 2017 Conference: Research and Development in Higher
Education: Curriculum Transformation, 40, (pp 12 – 24). Sydney, Australia,
27–30 June 2017.
Alammary, A., Sheard, J. & Carbone, A. (2014). Blended learning in higher
education: Three different design approaches. Australasian Journal of
Educational Technology, 30(4), 440-454. https://doi.org/10.14742/ajet.693
Alexander, I. D., & Fink, A. (2018). Designing an inclusive intercultural online
participatory seminar for Higher Education teachers and professionals. In
Bonderup Dohn, N., Cranmer, S., Sime, J-A., de Laat, M., & Ryberg, T.
(Eds). (2018). Networked learning: reflections and challenges (pp.125-148).
Switzerland: Springer. Research in Networked Learning.
https://doi.org/10.1007/978-3-319-74857-3_11
Al Dahdouh, A. A. (2018). Jumping from one resource to another: how do students
navigate learning networks? International Journal of Educational Technology
in Higher Education, 15(45). Retrieved from https://link.springer.com/article-
/10.1186/s41239-018-0126-x
Ally, M. (2008). Foundations for educational theory for online learning. In T.
Anderson (Ed.), The theory and practice of online learning (pp. 15-44).
Edmonton, AB: Athabasca University Press.
308 References
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 98(4),
369- 406.
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard
University Press. https://doi.org/10.1016/0004-3702(86)90084-6
Anderson, J. R. (1985). Cognitive psychology and its implications (2nd ed). New
York: Freeman.
Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American
Psychologist, 51(4), 355-365. https://doi.org/10.1037/0003-066X.51.4.355
Anderson, T. & Dron, J. (2011). Three generations of distance pedagogy.
International Review of Research in Open and Distance Learning, 12(3), 80-
97. https://doi.org/10.19173/irrodl.v12i3.890
Ariel, R., & Karpicke, J. D. (2018). Improving self-regulated learning with a retrieval
practice intervention. Journal of Experimental Psychology: Applied, 24, 43-
56. https://doi.org/10.1037/xap0000133
Artino, A. R. (2007). Self-regulated learning in online education: A review of the
empirical literature. International Journal of Instructional Technology and
Distance Learning, 4(6), 3–18.
ATC21sTM Assessment & teaching of 21st century skills. Headquartered at the
University of Melbourne. Sponsored by Cisco, Intel, Microsoft. ATC21C.
Retrieved from: http://www.atc21s.org/
Attar, M. (2018). Connectivism theory a noteworthy necessity in the process of
making schools smart. Proceedings: International Conference on
Psychology, Educational and Behavioral Sciences. Volume 3. Retrieved
from https://www.sid.ir/en/seminar/ViewPaper.aspx?FID=612E20180301
References 309
Attewell, J., Savill-Smith, C. & Douch, R. (2009). The Impact of mobile learning:
Examining what it means for teaching and learning. LSN. Retrieved from
http://media.cornwall.ac.uk/ostube/media/document/83.pd
ATSE, Australian Academy of Technology and Engineering (2016). Submission to
the House of Representatives standing committee on education and
employment. Inquiry into innovation and creativity: Workforce for the new
economy. March 2016. Retrieved from https://www.atse.org.au/Documents/-
submissions/inquiry-into-innovation-creativity.pdf
Australian Government NHaMRC. (2015). National statement on ethical conduct in
human research. Retrieved from: https://www.nhmrc.gov.au/guidelines-
publications/e72
Barnett, R., & Jackson, N. (Eds.) (2020). Ecologies for learning and practice:
Emerging ideas, sightings, and possibilities. Oxon, New York: Routledge.
https://doi.org/10.4324/9781351020268
Bassey, M. (1999). Case study research in educational settings. Open University
Press, Buckingham: Philadelphia.
Bates, A.W. (2015). Teaching in a digital age. Guidelines for designing teaching and
learning. Vancouver, BC: BC Campus. Retrieved from
https://opentextbc.ca/teachinginadigitalage/
Bawden, D. (2008) Origins and concepts of digital literacy. In C. Lankshear & M.
Knobel (Eds), Digital literacies: Concepts, policies and practices (pp.17-32).
New York, Berlin, Oxford: Peter Lang. Retrived from https://researchonline.-
jcu.edu.au/27788/1/27788_Lankshear_and_Knobel_2008.pdf
Beetham, H. & Sharpe, R. (Eds). (2013). Rethinking pedagogy for a digital age:
Designing and delivering e-learning (2nd ed.). Routledge Falmer: London.
Retrieved from https://joshuakoop.weebly.com/uploads/5/6/3/6/56367463/-
310 References
rethinking_pedagogy_for_a_digital_age.pdf
Bembenutty, H., White, M.C. & Vélez, M. (2015). Developing self-regulation of
learning and teaching skills among teacher candidates. Springer Briefs in
Education, 17(134), 9-28. https://doi.org/10.1007%2F978-94-017-9950-8
Bennett, D., Kapoor, R., Rajinder, K., & Maynard, N. (2015). First year engineering
students: Perceptions of engineers and engineering work amongst domestic
and international students. The International Journal of the First Year in
Higher Education, 6(1), 89-105. http://doi.org/10.5204/intjfyhe.v6i1.272
Berk, L. E. (2001). Development through the lifespan (2nd ed.). Boston, MA: Allyn &
Bacon.
Berlanga, A. J., Sloep, P. B., Kester, L., Brouns, F., van Rosmalen, P., & Koper, R.
(2008). Ad hoc transcient communities: Towards fostering knowledge
sharing in learning networks. International Journal of Learning Technology,
3(4), 443-458. Retrieved from https://pdfs.semanticscholar.org/e316/3cdf0d-
237d5a11aa13cb79a3bf2fad75d5ed.pdf
Bjork. R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs,
techniques, and illusions. Annual Review of Psychology, 64, 417-444.
http://doi.org/10.1146/annurev-psych-113011-143823
Bloor, M., Frankland, J., Thomas, M., & Robson, K. (2001). Focus groups in social
research. SAGE Publications.
Blunden, H. (n. d.). Active learning solutions website. Retrieved from http://activate-
learning.com.au/about-helen/
Boekaerts, M. (1996). Self-regulated learning at the junction of cognition and
motivation. European Psychologist, 1(2), 100–
112. https://doi.org/10.1027/1016-9040.1.2.100
References 311
Boekaerts, M. (1997). Self-regulated learning: a new concept embraced by
researchers, policy makers, educators, teachers, and students. Learn. Instruct.
7, 161–186. https://doi.org/10.1016/S0959-4752(96)00015-1
Bolhuis, S. (2003). Towards process-oriented teaching for self-directed lifelong
learning: A multidimensional perspective. Learning and Instruction, 13, 327-
347. https://doi.org/10.1016/S0959-4752(02)00008-7
Boud, D. & Hager, P. (2012). Re-thinking continuing professional development
through changing metaphors and location in professional practices. Studies in
Continuing Education, 34(1), 17–30. https://doi.org/10.1080/0158037X.-
2011.608656
Bowman, K. (2010). Background paper for the AQF council on generic skills. South
Australian Department of Further Education, Employment, Science and
Technology on behalf of the Australian Qualifications Framework Council,
Adelaide. Retrieved from http://www.aqf.edu.au/wpcontent/uploads/2013-
/06/Generic-skills-background-paper-FINAL.pdf
Boyer, S. L., Edmondson, D. R., Artis, A. B. & Fleming, D. (2013). Self-directed
learning: A tool for lifelong learning. Journal of Marketing
Education, 36(1), 20-32. https://doi.org/10.1177/0273475313494010
Brand-Gruwel, S., Kester, L., Kicken, W., & Kirschner, P. A. (2014). Learning
ability development in flexible learning environments. In J. M. Spector, M.
D. Merrill & J. Elen (Eds), Handbook of research on educational
communications and technology (pp. 363-372) (4th ed.). New York, NY:
Springer. https://doi.org/10.1111/jcal.12076
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn:
Brain, mind, experience, and school. (Expanded edition). Washington, DC:
National Academy Press. Retrieved from https://www.nap.edu/catalog/9853/-
how-people-learn-brain-mind-experience-and-school-expanded-edition
312 References
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic
achievement in online higher education learning environments: A systematic
review. The Internet and Higher Education, 27, 1–3. https://10.1016/j.iheduc-
.2015.04.007
Brockett, R. G., & Hiemstra, R. (1991). A conceptual framework for understanding
self-direction in adult learning. In R. G., Brockett & R., Hiemstra, Self-
direction in adult learning: Perspectives on theory, research, and practice
(pp.18-34). London and New York: Routledge.
Bronfenbrenner, U. (1977). Toward an experimental ecology of human development.
American Psychologist, 32(7), 513-531. https://doi.org/10.1037/0003-
066X.32.7.513
Bronfenbrenner, U. (1979). The ecology of human development: Experiments by
nature and design. Cambridge, MS: Harvard University Press. Retrieved
from https://khoerulanwarbk.files.wordpress.com/2015/08/urie_-
bronfenbrenner_the_ecology_of_human_developbokos-z1.pdf
Bronfenbrenner, U. (1986). Ecology of the family as a context of human
development: Research perspectives. Developmental Psychology, 22(6), 723-
742. https://doi.org/10.1037/0012-1649.22.6.723
Bronfenbrenner, U. (1994). Ecological models of human development. Readings on
the Development of Children, 2, 37-43. Retrived from http://edfa2402-
resources.yolasite.com/resources/Ecological%20Models%20of%20Human%
20Development.pdf
Bronfenbrenner, U. (2005). Ecological systems theory. In U. Bronfenbrenner (Ed.),
Making human beings human: Bioecological perspectives on human
development (pp. 106–173). Thousand Oaks, CA: Sage.
Bronfenbrenner, U., & Ceci, S.J. (1993). Heredity, environment, and the question of
“how?”: A new theoretical perspective for the 1990s. In R. Plomin & G. E.
References 313
McClearn (Eds.), Nature, nurture, and psychology (pp. 313-324).
Washington, DC: American Psychological Association.
https://doi.org/10.1037/10131-015
Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human
development. In R. M. Lerner & W. E. Damon (Eds.), Handbook of child
psychology: Vol 1, theoretical models of human development (6th ed., pp.
793-828). Chichester, United Kingdom: John Wiley and Sons.
Brooks, J. G., & Brooks, M. G. (1999). In search of understanding: The case for
constructivist classrooms. Alexandria, VA: Association for Supervision and
Curriculum Development.
Brown, Z. (Ed.), Ward, S. (Ed.). (2018). Contemporary Issues in Childhood. London:
Routledge, https://doi-org./10.4324/9781315513850
Brundrett, M., & Rhodes, C. (2014). Researching educational leadership and
management. London: Sage Publications.
Bruner, J. (1966). Toward a Theory of Instruction. Cambridge, MA: Harvard University Press.
Bruner, J. (1990). Acts of Meaning. Cambridge, MA: Harvard University Press.
Buckingham S. S., & Ferguson, R. (2012). Social learning analytics. Educational
Technology and Society, 15(3), 3-26. https://doi.org/10.1145/2330601-
.2330616
Cabrero, R. S., & Román, O. C. (2018). Psychopedagogical predecessors of
connectivism as a new paradigm of learning. International Journal of
Educational Excellence, 4(2), 29-45. https://doi.org/10.18562/IJEE.037
Campbell, G. (2016). Networked learning as experiential learning. Educause Review,
51(1), January 11. Retrieved from https://er.educause.edu/articles/2016/1-
/networked-learning-as-experiential-learning
314 References
Cameron, C., George, L., & Henley, M. (2012). All hands on deck: A team approach
to preparing year one arts students for their first major assignment. A practice
report. The International Journal of the First Year in Higher Education, 3(1),
101-108. https://doi.org/10.5204/intjfyhe.v3i1.117
Candy, P.C. (1991). Self-direction for lifelong learning: A comprehensive guide to
theory and practice. San Francisco, CA: Jossey-Bass. https://doi.org/10.-
1177/074171369204200307
Carroll, A., Lodge, J., Bagraith, R., Nugent, A., Matthews, K., & Sah, P. (2018).
Higher education learning framework matrix: An evidence informed model
for university learning. Brisbane: The University of Queensland.
https://doi.org/10.13140/RG.2.2.20761.47207
Carvalho, L. & Goodyear, P. (Eds) (2014a). The architecture of productive learning
networks. New York: Routledge. https://doi.org/10.4324/9780203591093
Carvalho, L., & Goodyear, P. (2014b). Synthesis: Set design, epistemic design and
the functioning of learning networks. In L. Carvalho, P. Goodyear (Eds.), The
architecture of productive learning networks (pp. 259-276). New York:
Routledge. https://doi.org/10.4324/9780203591093
Carvalho, L. & Goodyear, P. (2014c). Analysing the structuring of knowledge in
learning networks. In S. Bayne, C. Jones, M. de Laat, T. Ryberg & C. Sinclair
(Eds.). Proceedings of the 9th International Conference on Networked
Learning 2014 (pp.59-66). Retrieved from http://www.lancaster.-
ac.uk/fss/organisations/netlc/past/nlc2014/abstracts/pdf/carvalho.pdf
Carvalho, L. & Goodyear, P. (2018). Design, learning networks and service
innovation. Design Studies, 55, 27-53. https://doi.org/10.1016/j.destud-
.2017.09.003
References 315
Carvalho, L., Goodyear, P. & de Laat, M. (Eds.). (2017). Place-based spaces for
networked learning. New York and London: Routledge. https://doi.org/10.-
4324/9781315724485
Carvalho, L. & Saunders, R. (2018). Coding, designing and networking: fostering
learning through social connections. Research in Learning Technology,
26(2006). https://doi.org/10.25304/rlt.v26.2006
Casquero, O., Ovelar, R., Romo, J., Benito, M., & Alberdi, M. (2013). Students’
personal networks in virtual and personal learning environments. A case
study in higher education using learning analytics approach. Interactive
Learning Environments, 24(1), 49-67. https://doi.org/10.1080/-
10494820.2013.817441
Casquero, O., Benito, M., Romo, J., & Ovelar, R. (2016). Participation and
interaction in learning environments: A whole-network analysis. In K. Terry,
& A. Cheney (Eds.), Utilizing virtual and personal learning environments for
optimal learning (pp. 111-131). Hershey, PA: IGI Global. https://doi.org-
/10.4018/978-1-4666-8847-6.ch006
Castel A.D, McCabe D. P., & Roediger, H. (2007). Illusions of competence and
overestimation of associative memory for identical items: Evidence from
judgments of learning. Psychonomic Bulletin & Review, 14(1), 107–111.
https://doi.org/10.3758/BF03194036
Chatti, M. A., Jarke, M., & Specht, M. (2010). The 3P learning model. Educational
Technology & Society, 13(4), 74-85. Retrived from https://core.ac.uk/down-
load/pdf/55535986.pdf
Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for
differentiating learning activities. Topics in Cognitive Science, 1, 73–105.
https://doi.org/10.1111/j.1756-8765.2008.01005.x
316 References
Christie, M., O’Neill, M., Rutter, K., Young, G. & Medland, A. (2017).
Understanding why women are under-represented in Science, Technology,
Engineering and Mathematics (STEM) within Higher Education: a regional
case study. Production, 27(spe). https://doi.org/10.1590/0103-6513.220516
Clark, R. C., Nguyen, F. & Sweller, J. (2006). Efficiency in learning: Evidence-based
guidelines to manage cognitive load. San Francisco, CA: Pfeiffer.
Cleary, T., & Zimmerman, B.J. (2000). Self-regulation differences during athletic
practice by experts, nonexperts, and novices. Journal of Applied Sport
Psychology, 13, 61-82. https://doi.org/10.1080/104132001753149883
Cohen, L. & Magen-Nagar, N. (2016). Self-regulated learning and a sense of
achievement in MOOCs among high school science and technology students.
American Journal of Distance Education, 30(2), 68-79.
https://doi.org/10.1080/08923647.2016.1155905
Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th
ed.). London and New York: Routledge. Retrived from https://islmblogblog.-
files.wordpress.com/2016/05/rme-edu-helpline-blogspot-com.pdf
Cole F. L. (1988). Content analysis: process and application. Clinical Nurse
Specialist 2(1), 53–57. https://doi.org/10.1097/00002800-198800210-00025
Conole, G. (2008). New schemas for mapping pedagogies and technologies. Ariadne
56(2). Retrieved from: http://www.ariadne.ac.uk/issue/56/conole/
Cook-Sather, A., Bovill, C., & Felten, P. (2014). Engaging students as partners in
learning and teaching: A guide for faculty. San Francisco: Jossey-Bass.
Cowan, N. (2012). Working memory: the seat of learning and comprehension. In S.
Della Salla, & M. Anderson, M. (Eds.), Neuroscience in education: The
good, the bad and the ugly (pp. 111- 127). Oxford: Oxford University Press.
https:doi.org/10.1093/acprof:oso/9780199600496.001.0001
References 317
Creswell, J.W. (2007). Research design: Qualitative and mixed methods approaches.
London: Sage.
Creswell, J. W. (2014).
methods approaches (4th ed.). Thousand Oaks, California: SAGE
Publications.
Creswell, J.W. & Plano Clark, V.L. (2007). Designing and conducting mixed
methods research. London: Sage.
Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003).
Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie
(Eds.), Handbook of mixed methods in social and behavioral research (pp.
209–240). Thousand Oaks, CA: Sage.
Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed
methods research (2nd ed.). Thousand Oaks, CA: Sage.
Creswell, J. W., Klassen, A. C., Plano Clark, V. L., & Smith, K. C. (2011). Best
practices for mixed methods research in the health sciences. Retrieved from
https://obssr.od.nih.gov/training/online-training- resources/mixed-methods-
research/
Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology.
American Psychologist, 30(2), 116–27. https://doi.org/10.1037/h0076829
CSR Europe. (2016). EU STEM Coalition: STEM skills for a future proof Europe.
Fostering innovation, growth and jobs by bridging the EU STEM skills
mismatch. The Hague: STEM Coalition. Retrived from https://www.scribd-
.com/document/344483869/EU-STEM-Coalition-Brochure-2016
Czaplinski, I. (2013). Affordances of ICTs: an environmental study of a French
language unit offered at university level. Master of Philosophy thesis,
University of Queensland. https://doi.org/10.14264/uql.2017.394
318 References
Czaplinski, I. (2015). Investigating the effectiveness of an ecological approach to
learning design in a first year mathematics for engineering unit. In
Proceedings of the ASCILITE2015: 32nd International Conference on
Innovation, Practice and Research in the Use of Educational Technologies in
Tertiary Education: Globally Connected, Digitally Enabled (pp. 65-76). 29
November - 2 December 2015, Curtin University, Perth, W. A. Retrived from
http://www.2015conference.ascilite.org/wp-content/uploads/2015/11/ascilite-
2015-proceedings.pdf
Czaplinski, I., Mallet, D., Burrage, P. & Psaltis, S. (2015) Preparing engineering
graduates for the knowledge economy through blended delivery of
mathematics. In T. Thomas, E. Levin, P. Dawson, K. Fraser & R. Hadgraft
(Eds.), HERDSA 2015 Conference: Research and Development in Higher
Education: Learning for Life and Work in a Complex World, 38 (pp 107-
116). Melbourne, Australia. 6 - 9 July 2015. Retrived from file:///C:/Users-
/czaplins/Downloads/HERDSA_2015_Czaplinski.pdf
Czaplinski, I. & Mallet, D.G. (2016). Preparing future graduates to become lifelong,
expert learners: Lessons and considerations from a blended learning
engineering mathematics unit. In ACSME (Australian Conference on Science
and Mathematics Education) Proceedings: The 21st century science and
maths graduate (pp. 182-187), 28-30 September 2016, The University of
Queensland, Brisbane, QLD. Retrived from https://openjournals.library-
.sydney.edu.au/index.php/IISME/article/viewFile/10693/11278
Czaplinski, I., Sillence, M., Parsons, S., de Laat, M., Devine, C., Phillips, M. (2017).
What about me? Staff perspectives on the implementation of Intensive Mode
of Delivery in an undergraduate science program. In HERDSA 2017
Conference: Research and Development in Higher Education: Curriculum
Transformation (pp.106-116), 27-30 June 2017, International Convention
Centre, Sydney, NSW. Retrived from file:///C:/Users/czaplins/Downloads-
/Czaplinski_et_al_HERDSA2017.pdf
References 319
Czerniewicz, L., Williams, K., & Brown, C. (2009). Students make a plan:
Understanding student agency in constraining conditions. Research in
Learning Technology, 17, 75–88. Retrived from https://www.learntechlib-
.org/p/105467/
Czerniewicz, L. (2018). Inequality as Higher Education goes online. In Bonderup
Dohn, N., Cranmer, S., Sime, J-A., de Laat, M., & Ryberg, T. (Eds). (2018).
Networked learning: reflections and challenges (pp.95-108). Switzerland:
Springer. Research in Networked Learning. https://doi.org/10.1007/978-3-
319-74857-3_11
Dabbagh, N., & Kitsantas, A. (2012). Personal learning environments, social media,
and self-regulated learning: A natural formula for connecting formal and
informal learning. Internet and Higher Education, 15, 3-8. https://doi.org/-
10.1016/j.iheduc.2011.06.002
Daniel, J. S. (2016a). Making sense of blended learning: Treasuring an older
tradition or finding a better future? Online Learning News, Contact North -
March 2. Retrieved from http://teachonline.ca/sites/default/files/tools-
trends/downloads/making
Daniel, J. S. (2016b). Making sense of flexibility as a defining element of online
learning. Online Learning News, Contact North – September 8. Retrieved
from http://teachonline.ca/tools-trends/exploring-future-education/making-
sense-flexibility-defining-element-online-learning
Danziel, J. (2015). Reflections on the art and science of learning design and the
Larnaca Declaration. In M. Maina, B. Craft, Y. Mor (Eds.), The art & science
of learning design (pp. 3-14). Rotterdam: Sense Publishers.Retrieved from
https://www.sensepublishers.com/media/2398-the-art-and-science-
of_learning-design.pdf
Danziel, J., Conole, G., Wills, S., Walker, S., Bennett, S, Dobozy, E., Cameron, E.,
Badilescu-Buga, E. & Bower, M. (2016). The Larnaca declaration on learning
320 References
design – 2013. Journal of Interactive Media in Education, 2016(1), 7.
http://doi.org/10.5334/jime.407
Davidson, C., N. & Goldberg, D.T. (with Jones, Z.M.). (2010). The future of
thinking: learning institutions in a digital age. Cambridge, MA: MIT.
Retrieved from https://dmlhub.net/wpcontent/uploads/files/Future_of_-
Thinking.pdf
Davis, J. & Elliott, S. (2019). Aligning Bronfenbrenner’s model with UNESCO’s
dimensions of sustainability. Every Child, 25(1), 24-25. Retrieved from:
http://www.earlychildhoodaustralia.org.au/our-publications/every-child-
magazine/every-child-index/every-child-vol-25-no-1-2019/aligning-
bronfenbrenners-model-unescos-dimensions-sustainability/
Dekker, P. (2012). Presupposition. In G. Russell, D. G. Fara (Eds.). (2012).
Routledge Companion to Philosophy of Language, 42-52. New York:
Routledge, https://doi/10.4324/9780203206966
De Laat, M. & Dawson, S. (2017). Is there anybody out there? Place-based networks
for learning: NetMap – a tool for assessing hidden informal learning
networks. In L. Carvalho, P. Goodyear & M. de Laat (Eds.), Place-based
spaces for networked learning. New York and London: Routledge.
https://doi.org/10.-4324/9781315724485
De Laat, M., & Ryberg, T. (2018). Celebrating the tenth networked learning
conference: Looking back and moving forward. In N. B. Dohn, S.Cranmer,
J.-A.Sime, M. de Laat, T. Ryberg (Eds.), Networked Learning: reflections
and challenges (pp.1-20). Springer International Publishing AG.
https://doi.org/10.1007/978-3-319-74857-3
Della Salla, S. & Anderson, M. (Eds.) (2012). Neuroscience in education: The good,
the bad and the ugly. Oxford: Oxford University Press.
https:doi.org/10.1093/acprof:oso/9780199600496.001.0001
References 321
Department of Education and Training. (2017). Undergraduate applications offers
and acceptances 2017. Report. Retrieved from:
https://docs.education.gov.au/system/files/doc/other/undergraduate_applicatio
ns_offers_and_acceptances_2017.pdf
Department of Education and Training. (2018). Undergraduate applications offers
and acceptances 2018. Report. Retrieved from:
https://docs.education.gov.au/system/files/doc/other/undergraduate_applicatio
ns_offers_and_acceptances_2018.pdf
Díaz, J. A., & de Frutos, T. H. (2018). Connectivism in the network society: The
coming of social capital knowledge. Tendencias Sociales. Revista de
Sociología, 1(2018), 21-37. Retrieved from http://revistas.uned.es/index-
.php/Tendencias/article/view/21358/17565
Dieterle, E., & Clarke, J. (2008). Multi-user virtual environments for teaching and
learning. In M. Pagani (Ed.), Encyclopedia of multimedia technology and
networking (2nd ed) (pp. 1033-1041). Hershey, PA: Idea Group, Inc.
Retrieved from https://www.researchgate.net/publication/250016409_Multi-
User_Virtual_Environments_for_Teaching_and_Learning/download
Dignath, C., Buettner, G. & Langfeldt, H. P. (2008). How can primary school
students learn self-regulated learning strategies most effectively? A meta-
analysis on self-regulation training programmes. Educational Research
Review, 3(2), 101–129. https://doi.org/10.1016/j.edurev.2008.02.003
Dillenbourg, P. (1999). What do you mean by ‘collaborative learning?’ In P.
Dillenbourg (Ed.), Collaborative-learning: Cognitive and computational
Aaproaches (pp. 1–19). Oxford: Elsevier. Retrieved from https://telearn.-
archives-ouvertes.fr/hal-00190240/document
Dirckinck-Holmfeld, L. (2016). Networked learning and problem and project
based learning – how they complement each other. In S. Cranmer,
N.B. Dohn, M. de Laat, T. Ryberg & J.-A. Sime, (Eds.), Proceedings
322 References
of the 10th International Conference on Networked Learning 2016.
Lancaster University, U.K. (n. p.). Retrieved from http://www.net-
workedlearningconference.org.uk/abstracts/pdf/P13.pdf
Dirkx, K. J. H, Camp, G., Kester, L. & Kirschner, P. A. (2019). Do secondary school
students make use of effective study strategies when they study on their own?
Applied Cognitive Psychology, 33, 952-957. https://doi.org/10.1002/acp.3584
-Henderson, L, & Dlouhý, J. (2013). Learning networks with
involvement of higher education institutions. Journal of Cleaner Production,
49, 95-104. https://doi.org/10.1016/j.jclepro.2012.06.009
Dohn, N.B. (2014). Implications for networked learning of the ‘practice’ side of
social practice theories: A tacit-knowledge perspective. In V. Hodgson, M. de
Laat, D. McConnell, & T. Ryberg (Eds.). (2014). The design, experience and
practice of networked learning (pp. 29-50). New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
Dohn, N.B., Sime, J-A., Cranmer, S., Ryberg, T., & de Laat, M. (2018). Reflections
and challenges in networked learning. In N. B. Dohn, S.Cranmer, J.-A.Sime,
M. de Laat, T. Ryberg (Eds.), Networked learning: Reflections and
challenges (pp.187-212). Springer International Publishing AG.
https:doi.org/10.1007/978-3-319-74857-3
Dohn, N.B, Cranmer, S., Sime, J-A., de Laat, M., & Ryberg, T. (Eds). (2018).
Networked learning: reflections and challenges. Switzerland: Springer.
Research in Networked Learning. https://doi.org/10.1007/978-3-319-74857-
3_11
Donmoyer, R. (2008). Paradigm. In L.M. Given (Ed.). The Sage encyclopedia of
qualitative research methods (pp. 591-595). Thousand Oaks, CA: Sage.
Retrived from http://www.yanchukvladimir.com/docs/Library/Sage%20-
Encyclopedia%20of%20Qualitative%20Research%20Methods-
%202008.pdf
References 323
Dowling, M. (2006). Approaches to reflexivity in qualitative research. Nurse
Researcher, 13(3), 7-21. https:doi.org/10.7748/nr2006.04.13.3.7.c5975
Downes, S. (2007a). An introduction to connective knowledge. Paper presented at the
International Conference on Media, knowledge & education — exploring
new spaces, relations and dynamics in digital media ecologies. Retrieved
from http://www.downes.ca/post/33034
Downes, S. (2007b, February 03). What connectivism is? [Web log post]. Retrieved
from http://halfanhour.blogspot.com.au/2007/02/what-connectivism-is.html
Downes, S. (2008). Places to go: Connectivism & connective knowledge. Innovate,
5(1). Retrieved from http://www.innovateonline.info/pdf/vol5_issue1/Places-
_to_Go-Connectivism_&_Connective_Knowledge.pdf
Downes, S. (2010a). Learning networks and connective knowledge. In H. H. Yang &
S. C. Yuen, (Eds.), Collective intelligence and e-learning 2.0: implication of
web-based communities and networking (pp.1-26). IGI Global.
https://doi.org/10.4018/978-1-60566-729-4
Downes, S. (2010b). Connectivist learning and teaching. [Power Point slides]
Retrieved from http://www.slideshare.net/Downes/connectivist-learning-and-
teaching?next_slideshow=2
Downes, S. (2011). Connectivism and personalised learning. [Power Point slides]
Retrieved from http://www.slideshare.net/Downes/connectivism-and-
personal-learning
Downes, S. (2012). Connectivism and connective knowledge: Essays on meaning and
learning networks. National Research Council Canada, Creative Commons.
Retrieved from http://www.downes.ca/me/mybooks.htm
324 References
Downes, S. (2014, April 24). Connectivism as a learning theory. [Web log post]
Retrieved from http://halfanhour.blogspot.com.au/2014/04/connectivism-as-
learning-theory.html
Downes, S. (2015). From MOOC to personal learning. Revista FGV Online Year
5(1), 69-77. Retrieved from http://www.downes.ca/post/64556
Downes, S. (2016). Personal and personalized learning. EMMA Newsletter Feb 17,
2016. Retrieved from http://www.downes.ca/post/65065
Downes, S. (2019). Recent work in connectivism. European Journal of Open,
Distance and E-Learning. Retrieved from: http://www.eurodl.org/?p=current-
&sp=full&article=811
Drisoll, M. P. (2000). Psychology of learning for instruction. (2nd ed.). Needham
Heights, MA: Allyn and Bacon.
Duke, B., Harper, G., & Johnston, M. (2013). Connectivism as a digital age learning
theory. The International HETL Review, Special Issue, 2013, 4-13. Retrived
from https://pdfs.semanticscholar.org/9d49/9406ce42d07fc501c534eca-
528361ffe460f.pdf
Dunlosky, J., Rawson, K., A., Marsh, E.J., Nathan, M.J. & Willingham, D., T.
(2013). Improving students’ learning with effective learning techniques:
Promising directions from cognitive and educational psychology.
Psychological Science in the Public Interest, 14(1), 4-58.
https:doi.org/10.1177/1529100612453266
Dunlosky, J., & Tauber, S. (2016). The Oxford handbook of metamemory. New
York, NY: Oxford University Press. Retrieved from
https://www.oxfordhandbooks.-
com/view/10.1093/oxfordhb/9780199336746.001.0001/oxfordhb-
9780199336746
References 325
Dzubak, C. M. (2010). The cognition gap: Sufficient skills for high school but not
sufficient for college. Retrieved from http://www.myatp.org/wp-
content/uploads/2015/04/Synergy-Vol-4-Dzubak2.pdf
Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-
regulated learning: The MASRL model. Educational Psychologist, 46, 6 –
25. https://doi.org/10.1080/00461520.2011.538645
ELI, Educause Learning Initiative (2013). 7 things-you-should-know-about-
connected-learning. [Educause Web Document]. Retrieved from
(http://www.educause.edu/library/resources/7-things-you-should-know-
about-connected-learning).
Elliot, A. J., & Murayama, K. (2008). On the measurement of achievement goals:
Critique, illustration, and application. Journal of Educational Psychology,
100, 613–628. https://doi.org/10.1037/0022-0663.100.3.613
Ellis, R. & Goodyear, P. (2010). Students’ experiences of elearning in higher
education: the ecology of sustainable innovation. New York: Routledge
imprint of Taylor & Francis. https://doi.org/10.4324/9780203872970
Ellis, R., & Goodyear, P. (2019). The education ecology of universities: integrating
learning, strategy and the academy. New York: Routledge.
http://dx.doi.org/10.4324/9781351135863
Elo, S. & Kyngäs, H. (2008). The qualitative content analysis process. Journal of
Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-
2648.2007.04569.x
Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs,
H. (2014). Qualitative content analysis: A focus on
trustworthiness. SAGE Open, January-March 2014, 1-10.
https://doi.org/10.1177/2158244014522633
326 References
Engeström, Y. (2000). Activity theory as a framework for analysing and redesigning
work. Ergonomics, 43(7), 960-974. https://doi.org/10.1080/0014013004091-
43
Engeström, Y. (2015) (2nd ed.). Learning by expanding. Cambridge University Press.
Retrived from https://assets.cambridge.org/97811070/74422/frontmatter/-
9781107074422_frontmatter.pdf
Entwistle, N. (2000) Promoting deep learning through teaching and assessment:
Conceptual frameworks and educational contexts. Paper presented at 1st
Annual Conference ESRC Teaching and Learning Research Programme
(TLRP), University of Leicester, November 2000. Retrieved from:
http://www.tlrp.org/acadpub/Entwistle2000.pdf
Érdi, P. (2008). Complexity explained. Berlin, London: Springer. htpps://doi.org/
10.1007/978-3-540-35778-0
Eva, K. W. & Regehr, G. (2005). Self-assessment in the health professions: A
reformulation and research agenda. Academic Medicine, 80(10), October
2005 Supplement. Retrieved from https://journals.lww.com/academic-
medicine/Fulltext/2005/10001/Self_Assessment_in_the_Health_Professions_
_A.15.aspx Fettes, M. (2003). Critical realism, ecological psychology, and imagined
communities: Foundations for a naturalist theory of language acquisition. In
J. Leather & J. van Dam (Eds.), Ecology of language acquisition (pp. 31-47).
Boston, MA: Kluwer Academic Publishers. https://doi.org/ 10.1007/978-94-
017-0341-3
Fielding, N. G., & Lee, R. M. (1998). Computer analysis and qualitative research.
Thousand Oaks, CA: Sage.
Flick, U. (2009). An introduction to qualitative research (4th ed.). Los Angeles,
London: Sage. Retrived from https://mycourses.aalto.fi/pluginfile.php/551-
References 327
900/mod_resource/content/0/Uwe_Flick_An_Introduction_to_Qualitative_Re
search.pdf
Friedrich, R., Le Merle, M. Peterson, M. & Koster, A. (2010). The rise of generation
C: implications for the world of 2020’. Booz & Co, 1-20. Retrieved from:
http://www.booz.com/global/home/what-we-think/reports-white-
papers/article-display/rise-generation-implications-world-2020
Fung, D. (2017). A connected curriculum for higher education. London: UCL Press.
Retrieved from https://discovery.ucl.ac.uk/id/eprint/1558776/1/A-Connected-
Curriculum-for-Higher-Education.pdf
Fung, D. & Brent C. (2017). UCL connected curriculum: Enhancing programmes of
study (2nd ed.). University College London, UK. Retrieved from
www.ucl.ac.uk/connectedcurriculum
Fuson, K. C. (1979). The development of self-regulating aspects of speech: A
review. In G. Zivin (Ed.), The development of self-regulation through private
speech (pp.135–217). New York: Wiley.
wson, S., & Siemens, G. (2015). Let's not forget: Learning analytics
are about learning. TechTrends. 59(1), 64–71. https://doi.org/
10.1007/s11528-014-0822-x
Gelman, S.A. (2009). Learning from others: Children’s construction of concepts.
Annual Review of Psychology, 60, 115-140. https://doi.org/10.1146/
annurev.psych-.59.103006.093659
Gibson, J.J. (1977). The theory of affordances. In R.E. Shaw & J. Bransford (Eds.),
Perceiving, acting and knowing (pp.67-82). Hillsdale, NJ: Lawrence Erlbaum
Associates. Retrived from https://monoskop.org/images/2/2c/Gibson_-
James_J_1977_The_Theory_of_Affordances.pdf
328 References
Gibson, J. J. (1979). The ecological approach to visual perception. Mahwah, NJ:
Erlbaum.
Ginsbourg, H. P., & Opper, S. (2016). Piaget’s theory of intellectual development.
(3rd ed.). International Psychotherapy Institute E-books. Retrieved from
https://www.spc4-erp.com/uploads/about-1471606029-piagets-theory-of-
intellectual-development.pdf
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies
for qualitative research. Chicago: Aldine.
Gleerup, J., Heilesen, S., Helms, N. H., & Mogensen, K. (2014). Designing for
learning in coupled contexts. In V. Hodgson, M. de Laat, D. McConnell, & T.
Ryberg (Eds.). (2014). The design, experience and practice of networked
learning (pp. 51-66). New York: Springer. https://doi.org/10.1007/978-3-
319-01940-6_1
Good, J. M. M. (2007). The affordances for social psychology of the ecological
approach to social knowing. Theory & Psychology, 17(2), 265- 295.
https://doi.org/10.1177/0959354307075046
Goodfellow, R. (2011). Literacy, literacies, and the digital in higher education.
Teaching in Higher Education, 16(1), 131–144. https://doi.org/10.1080/135-
62517.2011.544125
Goodyear, P. (2000). Environments for lifelong learning: ergonomics, architecture
and educational design. In J. M. Spector & T. Anderson (Eds.). Integrated
and holistic perspectives on learning, instruction & technology:
Understanding complexity (pp. 1-18). Dordrecht: Kluwer Academic
Publishers. https://doi.org/10.1007/0-306-47584-7
Goodyear, P. (2014). Productive learning networks: The evolution of research and
practice. In L. Carvalho & P. Goodyear (Eds.), The architecture of productive
References 329
learning networks, (pp. 68-114). New York: Routledge. http://dx.doi.org/10.-
4324/9780203591093-14
Goodyear, P. (2015). Teaching as design. HERDSA Review of Higher Education,
2(2015), 27-50. Retrieved from http://www.herdsa.org.au/herdsa-review-
higher-education-vol-2/27-50
Goodyear, P. (2018). Design research: Health education in practice. Journal of
Research for Professional Learning, 1(1), 7-17. Retrieved from
https://openjournals.library.sydney.edu.au/index.php/HEP/article/view/12578
Goodyear, P., Banks, S., Hodgson, V., & McConnell, D. (2004). Advances in
research on networked learning. United Kingdom: Springer.
Goodyear, P., & Carvalho, L. (2013). The analysis of complex learning
environments. In H. Beetham & R. Sharpe (Eds). Rethinking pedagogy for a
digital age: Designing for 21st century learning (2nd ed.) (pp. 49-63). New
York: Routledge. http://dx.doi.org/10.4324/978020307895
Goodyear, P. & Carvalho, L. (2014a). Introduction: Networked learning and learning
networks. In L. Carvalho & P. Goodyear (Eds.), The architecture of
productive learning networks (pp. 3-22). New York: Routledge.
http://dx.doi.org/10.4324/9780203591093-10
Goodyear, P., & Carvalho, L. (2014b). Framing the analysis of learning network
architectures. In L. Carvalho & P. Goodyear (Eds.), The architecture of
productive learning networks (pp. 48-70). New York: Routledge.
http://dx.doi.org/10.4324/9780203591093-12
Goodyear, P. & Carvalho, L. (2016). Activity centred analysis and design in the
evolution of learning networks. In S. Cranmer S, N. B. Dohn, M. de Laat, T.
Ryberg & J. A. Sime (Eds.), Proceedings of the 10th International
Conference on Networked Learning 2016, (pp. 193-199). Retrieved from
http://www.networkedlearningconference.org.uk/abstracts/pdf/P13.pdf
330 References
Goodyear, P. & Dimitriadis, Y. (2013). In medias res: Reframing design for learning,
Research in Learning Technology, 21(SUPPL.1), 1-13. http://dx.doi.org/10.-
3402/rlt.v21i0.19909
Goodyear, P., Carvalho, L., & Dohn, N. B. (2016). Artefacts and activities in the
analysis of learning networks. In T. Ryberg, C. Sinclair, S. Bayne, & M. de
Laat (Eds.), Research, boundaries, and policy in networked learning, (pp. 93-
110). Springer. Research in Networked Learning. http://dx.doi.org/10.1007-
/978-3-319-31130-2_6
Goodyear, P., Hodgson, V. & Steeples, C. (1998). Student experiences of networked
learning in higher education: Research proposal to the UK JISC, October
1998. Lancaster: Lancaster University.
Goodyear, P., & Retalis, S. (2010). Learning, technology and design. In P. Goodyear,
S. Retalis (Eds.), Technology- Enhanced Learning: Design Patterns and
Pattern Languages, (pp. 1-27). Rotterdam: Sense Publishers.
Goodyear, P., Thompson, K., Ashe, D., Pinto, A., Carvalho, L., Parisio, M., Parker,
P., Schwendimann, B., Wardak, D., Yeoman, P. (2015). Analysing the
structural properties of learning networks: Architectural Insights into
Buildable Forms. In Marcelo Maina, Brock Craft, Yishay Mor (Eds.), The
Art & Science of Learning Design, (pp. 15-29). Rotterdam: Sense Publishers
http://dx.doi.org/10.1007/978-94-6300- 103-8_2
Greeno, J.G. (1994). Gibson’s Affordances. Psychological Review, 101(2), 336-342.
https://doi.org/10.1037
Griffin, P., & Care, E. (Eds). (2015). Assessment and teaching of 21st century skills:
methods and approach. Springer Netherlands, Dordrecht. https:doi.org/10-
.1007/978-94-017-9395-7
References 331
Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research.
In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative
research (pp. 105-117). Thousand Oaks, CA, US: Sage Publications, Inc.
Hagel, P. (2015). Towards an understanding of ‘Digital Literacy(ies)’. Discourse:
Deakin University Library Research & Practice, no. 1, Geelong, Deakin
University Library. Retrieved from http://dro.deakin.edu.au/view/-
DU:30073198
Hansen, J.J., & Dohn, N.B. (2018). Analysing learning designs of 'learning through
practice' as networked learning. In M. , N. B. Dohn, M. de Laat, P.
& T. Ryberg (Eds.), Proceedings of the 11th International
Conference on Networked Learning 2018 pp. 447-454. Retrieved from:
http://networkedlearningconference.org.uk/abstracts/papers/hansen_58.pdf
Hatch, J. A. (2002). Doing qualitative research in education settings. Albany: SUNY
Press.
Hannon, J. (2014). Making the right connections: Implementing the objects of
practice into a network of learning. In V. Hodgson, M. de Laat, D.
McConnell, & T. Ryberg (Eds.). (2014). The design, experience and practice
of networked learning (pp. 67-86). New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
Harrington, D. (2002). Emergence: The connected lives of ants, brains, cities, and
software interface: The Journal of Education, Community and Values 2(1).
Retrieved from http://commons.pacificu.edu/cgi/viewcontent.cgi?article-
=1006-&context=inter02
Hartson, H. R. (2003). Cognitive, physical, sensory, and functional affordances in
interaction design. Behaviour & Information Technology, 22(5), 31-38.
ttps://doi.org/10.1080/01449290310001592587
332 References
Hattie, J. A. C. & Donoghue, G. M. (2016). Learning strategies: a synthesis and
conceptual model. npj Science of Learning, 1, 1-13. https://doi.org/
http://10.0.4.14/npjscilearn.2016.13
Hayes, S. (2016). Learning from deceptively spacious policy discourse. In Ryberg,
T., Sinclair, Ch., Bayne, S., & de Laat, M. (Eds.). (2016). Research,
boundaries, and policy in networked learning (pp.23-40). Switzerland:
Springer. Research in Networked Learning. https://doi.org/10.1007/978-3-
319-31130-2
Hayes, N., O'Toole, L., Halpenny, A. (2017). Introducing Bronfenbrenner. London: Routledge, https://doi-org./10.4324/9781315646206
Heinrich, E. (2017). Teaching groups as midlevel sociocultural contexts for
developing teaching and learning: a case study and comparison to
microcultures. Higher Education Research & Development, 36(4), 702–715.
https://doi.org/10.1080/07294360.2016.1208641
Hesse-Biber, S. N. (Ed.) (2007). Handbook of feminist research: Theory and praxis.
Thousand Oaks, CA: Sage Publications Inc.
Hitchcock D. H., Hitchcock, G. & Hughes, D. (1995) Research and the teacher (2nd
ed.). London: Routledge.
Hodgson, V. & McConnell, D. (2018). The epistemic practice of networked
learning. . In M. , N. B. Dohn, M. de Laat, P. & T. Ryberg
(Eds.). Proceedings of the 11th International Conference on Networked
Learning 2018 (pp. 445-464). Retrieved from http://networkedlearning-
conference.org.uk/abstracts/papers/mcconnell_59.pdf
Holmes, B., & Sime, J.-A. (2014). Online learning communities for teachers’
continuous professional development: An action research study of e-twinning
learning events. In In V. Hodgson, M. de Laat, D. McConnell, & T. Ryberg
(Eds.). (2014). The design, experience and practice of networked learning
References 333
(pp. 185-206). New York: Springer. https://doi.org/10.1007/978-3-319-
01940-6_1
Hodgson, V., De Laat, M., McConnell, D., & Ryberg, T., (Eds.). (2014). The design,
experience and practice of networked learning. New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
Hopkins, D. (2002). A teacher’s guide to classroom research. (3rd ed.). Buckingham-
Philadelphia: Open University Press.
Hopper, P. J. (1998). Emergent grammar. In M. Tomasello (Ed.), The new
psychology of language: Cognitive and functional approaches to language
structure. Mahwah, NJ: Erlbaum.
Hutchby, I. (2001). Conversation and technology: from the telephone to the internet.
Polity Press: Cambridge.
Illich, I. (1971). Deschooling society. New York, NY: Harper Row.
Jackson, N. & Barnett, R. (2020). Steps to ecologies for learning and practice. In R.
Barnett & N. Jackson (Eds.) Ecologies for learning and practice: Emerging
ideas, sightings, and possibilities (pp. 14-26). Oxon, New York: Routledge.
https://doi.org/10.4324/9781351020268
, P. (2016). The methodological challenge of networked learning:
(Post)disciplinarity and critical emancipation. In Ryberg, T., Sinclair, Ch.,
Bayne, S., & de Laat, M. (Eds.). (2016). Research, boundaries, and policy in
networked learning (pp.165-182). Switzerland: Springer. Research in
Networked Learning. https://doi.org/10.1007/978-3-319-31130-2
Boras, D., (Eds.), (2015). Critical learning in digital networks.
Dordrecht: Springer. https://doi.org/10.1007/978-3-319-13752-0
334 References
Jochems, W. M. G., van Merriënboer, J. J. G., & Koper R. (Eds.) (2004). Integrated
e-learning: implications for pedagogy, technology and organization. London,
England: Routledge Falmer.
Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015). NMC Horizon
report: 2015 Higher education edition. Austin, Texas: The New Media
Consortium. Retrived from https://files.eric.ed.gov/fulltext/ED559357.pdf
Johnsson, M. C., Boud, D., & Solomon, N. (2012). Learning in-between, across and
beyond workplace boundaries. International Journal of Human Resources
Development and Management, 12(1-2), 61–76.
https://doi.org/10.1504/IJHRDM-.2012.044200
Jonassen, D. (1999). Designing constructivist learning environments. In C. M.
Reigeluth (Ed.), Instructional design theories and models: A new paradigm of
instructional theory, Vol. 2 (pp. 215–239). Hillsdale, NJ: Lawrence Erlbaum
Associates. Retrieved from http://www.davidlewisphd.com/courses/EDD-
8121/readings/1999-Jonassen.pdf
Johansson, J., Contero, M., Company P., & Elgh, F. (2018). Supporting connectivism
in knowledge based engineering with graph theory, filtering techniques and
model quality assurance. Advanced Engineering Informatics, 38, 252-263.
https://doi.org/10.1016/j.aei.2018.07.005
Jones, C. (2015). Networked learning: An educational paradigm for the age of
digital networks. Dordrecht: Springer. https://doi.org/10.1007/978-3-319-
01934-5
Jones, A. & Issroff, K. (2005). Learning technologies: Affective and social issues in
computer supported collaborative learning. Computers & Education, 44(4),
395–408. https://doi.org/10.1016/j.compedu.2004.04.004
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.
References 335
Kahu, E.R., & Picton, C. (2019). The benefits of good tutor-student relationships in
the first year. Student Success, 10(2), 23-33. https://doi.org/10.5204/ssj.v10-
i2.1293
Karpicke, J. D. (2012). Retrieval-based learning: active retrieval promotes
meaningful learning. Current Directions in Psychological Science, 21(3),
157-163. https://doi.org/10.1177/0963721412443552
Karpicke, J. D., & Blunt, J. R. (2011). Retrieval practice produces more learning than
elaborative studying with concept mapping. Science, 331, 772-775.
https://doi.org/10.1126/science.1199327
Karpicke, J. D., Butler, A. C., & Roediger, H. L. III. (2009). Metacognitive strategies
in student learning: Do students practice retrieval when they study on their
own? Memory, 17, 471–479. http://dx.doi.org/10.1080/09658210802647009
Katane, I. (2007). Systemic ecological approach in teacher education: Ecological
didactic model of students’ pedagogical practice. Journal of Teacher
Education for Sustainability, 7, 40-54. https://doi.org/10.2478/v10099-009-
0004-7
Kelly, A. E. (2003). Theme issue: The role of design in educational research.
Educational Researcher, 32(1), 3-4. https://doi.org/10.3102/0013189-
X032001003
Kelly, A.E., Baek, J.Y., Lesh, R., A. & Bannan-Ritland, B. (2008). Enabling
innovations in education and systematizing their impact. In A. E. Kelly, R. A.
Lesh & J. Y. Baek (Eds), Handbook of design research methods in education:
Innovations in science, technology, engineering and mathematics learning
and teaching (pp. 3-18). New York: Routledge. https://doi.org/10.-
4324/9781315759593
Keppell, M. (2014). Personalised learning strategies for higher education. In K.Fraser
(Ed.) The future of learning and teaching in next generation learning spaces.
336 References
International Perspectives on Higher Education Research, Volume 12 (pp. 3-
21). Emerald Group Publishing Limited. doi: 10.1108/S14793628_2014-
_0000012001
Keppell, M., & Riddle, M. (2012). Distributed learning places: Physical, blended and
virtual learning spaces in higher education. In M. Keppell, K. Souter, & M.
Riddle (Eds.), Physical and virtual learning spaces in higher education:
Concepts for the modern learning environment (pp. 1-20). Hershey:
Information Science Publishing. https://doi.org/10.4018/978-1-60960-114-0
Kerr, B. (2006). A challenge to conectivism [Web log post]. Retrieved from
http://billkerr2.blogspot.com.au/2006/12/challenge-to-connectivism.html
King, A. (1993). From sage on the stage to guide on the side. College Teaching,
41(1), 30-35. Retrieved from http://www.jstor.org/stable/27558571
Kirkwood, A. (2014). Teaching and learning with technology in higher education:
Blended and distance education needs ‘joined-up thinking’ rather than
technological determinism. Open Learning: The Journal of Open, Distance
and e-Learning, 29(3), 206-221. https://doi.org/10.1080/02680513.2015-
.1009884
Kirkwood, A., & Price, L. (2006). Adaptation for a changing environment:
Developing learning and teaching with information and communication
technologies. The International Review of Research in Open and Distributed
Learning, 7(2). https://doi.org/10.19173/irrodl.v7i2.294
Kirschner, P.A. (1992). Epistemology, practical work and academic skills in science
education. Science and Education, 1, 273-
299. http://dx.doi.org/10.1007/BF00430277
Kirschner, P. (2002). Can we support CSCL? Educational, social and technological
affordances for learning. In P. Kirschner (Ed.), Three worlds of CSCL: Can
we support CSCL. Inaugural address, Open University of the Netherlands.
References 337
Retrieved from http://www.ou.nl/Docs/Expertise/OTEC/Publicaties/paul-
kirschner/oratieboek_PKI_DEF_Klein_ZO.pdf.
Kirschner, P. A. (2009). Epistemology or pedagogy, that is the question. In S. Tobias
& T. M. Duffy (Eds.), Constructivist theory applied to instruction: Success or
failure? (pp. 144-157). New York, London: Routledge.
Kirschner, P. A. & Stoyanov, S. (2018). Educating youth for nonexistent/not yet
existing professions. Educational Policy, 1-41. https://doi.org/10.1177-
/0895904818802086
Kirschner, P., Strijbos, J.-W., & Kreijns, K. (2004). Designing integrated
collaborative e-learning. In Jochems, J. J. G van Merriënboer, & R. Koper
(Eds.) (2004). Integrated e-learning: implications for pedagogy, technology
and organization. London, England: Routledge Falmer.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during
instruction does not work: An analysis of the failure of Constructivist,
Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching.
Educational Psychologist, 41(2), 75-86. https://doi.org/10.1207/s153-
26985ep4102_1
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2016, April).
Recommending self-regulated learning strategies does not improve
performance in a MOOC. Paper presented at Learning@Scale 2016,
Edinburgh, Scotland, UK. Retrieved from: https://rene.kizilcec.com/wp-
content/uploads/2016/02/kizilcec2016recommending.pdf
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated
learning strategies predict learner behavior and goal attainment in Massive
Open Online Courses. Computers & Education, 104, 18–33. https://doi.org-
/10.1016/j.compedu.2016.10.001
338 References
Knowles, M.S. (1975). Self-directed learning: A guide for learners and teachers.
Association Press, New York, NY.
Konnerup, U., Ryberg, T., & Sørensen, M. T. (2018). The teacher as designer? What
is the role of ‘learning design’ in networked learning? In B. Milan, N. B.
Dohn, M. de Laat, P. Jandric, & T. Ryberg (Eds.), Proceedings of the 11th
International Conference on Networked Learning 2018 (pp. 331-339).
Kop, R. (2011). The challenges to connectivist learning on open online networks:
Learning experiences during a massive open online course. The International
Review of Research in Open and Distributed Learning, 12(3), 19-38.
https://doi.org/10.19173/irrodl.v12i3.882
Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of
the past? The International Review of Research in Open and Distributed
Learning, 9(3), 1-13. https://doi.org/10.19173/irrodl.v9i3.523
Koriat, A., & Bjork, R.A. (2005). Illusions of competence in monitoring one’s
knowledge during study. Journal of Experimental Psychology and
Cognition, 33(2), 187-194. https://doi.org/10.1037/0278-7393.31.2.187
Kornell, N., & Bjork, R.A. (2007). The promise and perils of self-regulated study.
Psychonomic Bulletin & Review, 14(2), 219-224. https://doi.org/10.3758-
/BF03194055
Kornell, N., Rhodes, M. G., Castel, A. D., & Tauber, S. K. (2011). The ease-of-
processing heuristic and the stability bias: Dissociating memory, memory
beliefs, and memory judgments. Psychological Science, 22, 787–794.
http://dx.doi.org/10.1177/0956797611407929
Krause, K., & Coates, H. (2008). Students’ engagement in first year university.
Assessment and Evaluation in HigherEducation, 33, 493–505.
https://doi.org/10.1080/02602930701698892
References 339
Krueger, R. A., & Casey, M. A. (2000). Focus groups: A practical guide for applied
research. London: Sage.
Kultawanich, K., Koraneekij, P., & Na-Songkhl, J. (2015). A proposed model of
connectivism learning using cloud-based virtual classroom to enhance
information literacy and information literacy self-efficacy for undergraduate
students. Social and Behavioral Sciences, 191, 87-92. https://doi.org/10.-
1016/j.sbspro.2015.04.394
Kumpulainen, K., & Sefton-Green, J. (2014). What is connected learning and how to
research it? International Journal of Learning and Media, 4(2), 7-18.
https://doi.org/10.1162/IJLM_a_00091
Kuhn, D. (2016). Learning is the key twenty-first century skill. Learning: Research
and Practice, 2(2), 88-99. https://doi.org/10.1080/23735082.2016.1205207
Lafond, F. (2015). Self-organization of knowledge economies. Journal of Economics
& Control, 52, 150-165. https://doi.org/10.1016/j.jedc.2014.12.004
Lai, E. (2011). Collaboration: A literature review. Upper Saddle River, NJ: Pearson.
Retrieved 15th of October 2016 from: http://images.pearsonassessments-
.com/images/tmrs/Collaboration-Review.pdf
Lamb, S., Maire, Q., & Doecke, E. (2017). Key skills for the 21st century: an
evidence-based review. Future Frontiers Analytical Report. NSW Department
of Education. Retrieved from https://education.nsw.gov.au/our-
priorities/innovate-for-the-future/education-for-a-changing-world/research-
findings/future-frontiers-analytical-report-key-skills-for-the-21st-
century/Key-Skills-for-the-21st-Century-Analytical-Report.pdf.
Land, R., Meyer, J. H. F. & Smith, J. (Eds.). (2008), Threshold concepts within the
disciplines. Rotterdam: Sense Publishers. Retrieved from https://www.sense-
publishers.com/media/1179-threshold-concepts-within-the-disciplines.pdf
340 References
Lankshear, C., & Knobel, M. (Eds). (2008). Digital literacies: Concepts, policies and
practices: New York, Berlin, Oxford: Peter Lang. retrieved from
https://researchonline.jcu.edu.au/27788/1/27788_Lankshear_and_Knobel_20
08.pdf
Laurillard, D. (2012) Teaching as a design science: Building pedagogical patterns
for learning and technology, New York: Routledge. https://doi.org/10.4324-
/9780203125083
Lee, K. (2018). Discursive effects of a paradigm shift rhetoric in online Higher
Education: Implications on networked learning research and practice. In
Bonderup Dohn, N., Cranmer, S., Sime, J-A., de Laat, M., & Ryberg, T.
(Eds). (2018). Networked learning: reflections and challenges (pp.57-78).
Switzerland: Springer. Research in Networked Learning.
https://doi.org/10.1007/978-3-319-74857-3_11
Lee, D., Lee, S. L., & Watson, W.R. (2019). Systematic literature review on self-
regulated learning in massive open online courses. Australasian Journal of
Educational Technology, 35(1), 28-41. https://doi.org/10.14742/ajet.3749
Leech, N. L., & Onwuegbuzie, A. J. (2007). An array of qualitative data analysis
tools: A call for data analysis triangulation. School Psychology Quarterly,
22(4), 557-584. https://doi.org/10.1037/1045-3830.22.4.557
Littlejohn, A., Jaidmark, J., Vrieling-Teunter, E., & Nijland, F. (Eds.)
(2019). Networked professional learning: Emerging and equitable
discourses for professional development. Cham:
Springer. https://doi.org/10.1007/978-3-030-18030-0.
Lincoln, S.Y., & Guba, E. G. (1985). Naturalistic inquiry. Thousand Oaks, CA:
Sage.
Lodge, J. M., Panadero, E., Broadbent, J., & de Barba, P. (2019). Supporting self-
regulated learning with learning analytics. In J. M. Lodge, J. Cooney Horvath
References 341
and L. Corrin (Ed.), Learning analytics in the classroom: translating learning
analytics research for teachers (pp. 45-55) Abingdon, Oxon, United
Kingdom: Routledge. https://doi.org/10.14742/ajet.3749
Løkse, M., Låg, T., Solberg, M., Andreassen, H.N., & Stenersen, M. (2017).
Teaching information literacy in higher education: Effective teaching and
active learning. Elsevier Science, 2017, Chandos Publishing.
Madden, M. & Fox, S. (2006). Riding the waves of Web 2.0: More than a buzzword,
but still not easily defined. Pew Research Centre. Internet & Technology.
Retrieved from: https://www.pewinternet.org/2006/10/05/riding-the-waves-
of-web-2-0/
Magen-Nagar, N. & Cohen, L. (2016). Learning strategies as a mediator for
motivation and a sense of achievement among students who study in
MOOCs. Education and Information Technologies, 22(3), 1271-1290.
https://doi.org/10.1007/s10639-016-9492-y
Maggio, L., Saltarelli, A., & Stranack, K. (2016). Crowdsourcing the curriculum: a
MOOC for personalised, connected learning. EDUCAUSE Review, March
2016. Retrieved from http://er.educause.edu/articles/2016/3/crowdsourcing-
the-curriculum-a-mooc-for-personalized-connected-learning
Malterud, K. (2001). Qualitative research: Standards, challenges and guidelines. The
Lancet. (358), 483-488.
Manning, R. (2017). Place-consciousness and Bronfenbrenner’s Ecological Systems
Model: A discussion of recurring issues that undermine the teaching of
indigenous histories in New Zealand and Australian Schools. The Australian
Journal of Indigenous Education, 46(2), 148–159. https://doi.org/10.1017-
/jie.2016.31
342 References
Margaryan, A., Littlejohn, A. & Vojt, G. (2011). Are digital natives a myth or
reality? University students’ use of digital technologies. Computers &
Education, 56, 429-440. https://doi.org/10.1016/j.compedu.2010.09.004
Marginson, S, Tytler, R, Freeman, B & Roberts, K (2013). STEM: Country
comparisons. Report for the Australian Council of Learned Academies,
www.acola.org.au
Markauskaite, L. (2018). Quality multiplied: Learning that matters in a
runaway world. Keynote for the Open Learning Conference 2018, Kuala
Lumpur, Malaysia. Retrieved from: https://epistemicfluency.com/2018-
/11/26/quality-multiplied-learning-that-matters-in-a-runaway-world/
Markauskaite, L. & Goodyear, P. (2017a). Epistemic fluency and professional
education: innovation, knowledgeable action and actionable knowledge.
Dordrecht: Springer. https://doi.org/10.1007/978-94-007-4369-4
Markauskaite, L. & Goodyear, P. (2017b). Preparing students for the workplace
through designing productive assessment tasks: An actionable knowledge
perspective. In R.G. Walker & S.B. Bedford (Eds.), Research and
development in higher education: Curriculum transformation, 40 (pp 198 -
208). Sydney, Australia, 27–30 June 2017.
Markauskaite, L. & Goodyear, P. (2018a). Universities should take stronger
leadership on knowledge and how it matters. The Conversation online. [Web
log post]. Retrieved from http://theconversation.com/universities-should-
take-stronger-leadership-on-knowledge-and-how-it-matters-89849
Markauskaite, L. & Goodyear, P. (2018b). Epistemic fluency blog. [Web log post]
Retrieved from: https://epistemicfluency.com/2018/04/12/universities-should-
take-stronger-leadership-on-knowledge-and-how-it-matters/#more-1198
Marmaridou, S. (2009). Presupposition. In L. Cummings (Ed.), The Routledge
pragmatics encyclopedia (pp. 349-353). London: Routledge.
References 343
Martin, A. (2008). Digital literacy and the “digital society”. In C. Lankshear & M.
Knobel (Eds), Digital literacies: Concepts, policies and practices (pp. 151-
176). New York, Berlin, Oxford: Peter Lang.
Martin, J. P. (2018). Skills for the 21st century: Findings and policy lessons from the
OECD survey of adult skills. OECD Education Working papers, No. 166,
OECD Paris. Retrieved from http://www.oecd.org/officialdocuments-
/publicdisplaydocumentpdf/?cote=EDU/WKP(2018)2&docLanguage=En
Marton F. & Säljö R. (1976). On qualitative differences in learning. I – Outcome and
Process. British Journal of Educational Psychology, 46, 4-11. https://doi.org-
/10.1111/j.2044-8279.1976.tb02980.x
McCabe, J. (2011). Metacognitive awareness of learning strategies in
undergraduates. Memory & Cognition, 39, 462-476. https://doi.org/10.3758-
/s13421-010-0035-2
McCaffrey, J., & Machery, E. (2012). Philosophical issues about concepts. WIREs
CognSci 2012, 3, 265-279. https://doi.org/10.1002/wcs.1166
McClelland, J. L. (2010). Emergence in cognitive science. Topics in Cognitive
Science, 2(2010), 751–770. https://doi.org/10.1111/j.1756-
8765.2010.01116.x
McGaw, B. (2013). Developing 21st century competencies through disciplines of
knowledge. International Symposium on Education and 21st Century
Competencies. Muscat, Sultanate of Oman, 22-14 September 2013, Oman.
Retreived from https://docs.acara.edu.au/resources/Developing_21st_century-
_competencies_Prof_Barry_McGaw.pdf
McKenney, S., & Reeves, T. C. (2012). Conducting educational design research.
London: Routledge. https://doi.org/10.4324/9781315105642
344 References
McKenney, S., Nieveen, N., & van den Akker, J. (2006). Design research from a
curriculum perspective. In J. van den Akker, K. Gravemeijer, S. McKenney,
& N. Nieveen (Eds.), Educational design research (pp.62-90). London:
Routledge. Retrieved from https://slo.nl › publish › pages › educational-
design-research-part-a
McLoughlin, C. & Lee, M. J. W. (2010). Personalised and self regulated learning in
the Web 2.0 era: International exemplars of innovative pedagogy using social
software. Australian Journal of Educational Technology, 26(1), 28-43.
https://doi.org/10.14742/ajet.1100
Meltzoff, A. N., Kuhl, P. K., Movellan, J., & Sejnowski, T., J. (2009). Foundations
for a new Science of learning. Science, 325(5938), 284-288. https://doi.org-
/10.1126/science.1175626
Merriam, S. B. (2001). Andragogy and self-directed learning: pillars of adult learning
theory. New Directions for Adult and Continuing Education, 89, pp. 3-13.
https://doi.org/10.1002/ace.3
Meyer, J. H. F., & Land, R. (2005). Threshold concepts and troublesome knowledge
(2): Epistemological considerations and a conceptual framework for teaching
and learnig. Higher Education, 49, 373-388. https://doi.org/10.1007/s10734-
004-6779-5
Meyer, J. H. F., & Land, R. (2006). Overcoming barriers to student understanding:
Threshold concepts and troublesome knowledge. London, New York:
Routledge. https://doi.org/10.4324/9780203966273
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded
source book. London: Sage.
Miller, G. (2011). The brain’s social network. Science, 334(6056), 578-789. doi:
10.1126/science. 334.6056.578
References 345
Milligan, C., Littlejohn, A., & Margaryan, A. (2014). Workplace learning in informal
networks. Journal of Interactive Media in Education, 1(6). http://dx.doi.-
org/doi:10.5334/2014-06
Mills, A. J., Durepos, G., & Wiebe, E. (2010). Encyclopedia of case study
research (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc.
https://doi.org/10.4135/9781412957397
Mlasi, S. M., & Naidoo, R. (2018). An exploratory study of the ODL course in
structural engineering. Proceedings of the 10th International Conference on
Computer Supported Education (CSEDU 2018) – Volume 2, 246-251.
Retrieved from https://www.scitepress.org/papers/2018/67513/67513.pdf
Mor, Y. & Craft, B. (2012). Learning design: Reflections upon the current landscape.
Research in Learning Technology, 20, 85-94.
https://doi.org/10.3402/rlt.v20i0.19196
Mor Y., Mellar H., Warburton S., & Winters N. (2014) Introduction. In: Y. Mor,
H. Mellar, S. Warburton, & N. Winters (Eds.). Practical design patterns
for teaching and learning with technology: Trails in education
(Technologies that support navigational learning). Sense Publishers,
Rotterdam. https://doi.org/10.1007/978-94-6209-530-4
Moskal, P., Dziuban, C., & Hartman, J. (2013). Blended learning: A dangerous idea?
Internet and Higher Education, 18, 15–23. https://doi.org/10.1186/s41239-
017-0087-5
Neelen, M. & Kirschner, P. A. (2015). Learning the smart way. 3-star learning
experiences. An evidence-informed blog for learning professionals [Web log
post]. Retrieved from: https://3starlearningexperiences.wordpress.com/2015-
/07/14/learning-the-smart-way-2/
346 References
Neelen, M. & Kirschner, P. A. (2016). 21st century skills don’t exist. So why do we
need them? 3-star learning experiences. An evidence-informed blog for
learning professionals [Web log post]. Retrieved from: https://3starlearning-
experiences.wordpress.com/2016/11/01/21st-century-skills-dont-exist-so-
why-do-we-need-them/
Neelen, M. & Kirschner, P. A. (2017). How to support self-directed learning in a
learning organisation? 3-star learning experiences. An evidence-informed
blog for learning professionals. [Web log post] Retrieved from: https://3star-
learningexperiences.wordpress.com/2017/03/21/how-to-support-self-
directed-learning-in-a-learning-organisation/
Neelen, M. & Kirschner, P. A. (2018a). Domain-specific knowledge:1, Domain-
independent skills :0. 3-star learning experiences. An evidence-informed blog
for learning professionals. [Web log post] Retrieved from: https://3star-
learningexperiences.wordpress.com/2018/07/10/domain-specific-knowledge-
1-domain-independent-skills-0/
Neelen, M. & Kirschner, P. A. (2018b). How to effectively build and leverage a
personal learning network (PLN). 3-star learning experiences. An evidence-
informed blog for learning professionals. [Web log post] Retrieved from:
https://3starlearningexperiences.wordpress.com/2018/09/04/how-to-
effectively-build-and-leverage-a-personal-learning-network-pln/
Nelson, K., Clarke, J., & Creagh, T. (2014). Five years on. Editorial. The
International Journal of the First Year in Higher Education, 6(1), 1-3.
https://doi.org/10.5204/intjfyhe.v5i2.243
Newen, A., & Bartels, A. (2007). Animal minds and the possession of concepts.
Philosophical Psychology, 20(3), 283-308. https://doi.org/10.1080/09515080-
701358096
Nicolajsen, H.W. (2014). Changing the rules of the game: Using blogs for online
discussions in Higher Education. In Hodgson, V., De Laat, M., McConnell,
References 347
D., & Ryberg, T., (Eds.). (2014). The design, experience and practice of
networked learning (pp. 149-164). New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
Nieveen, N., & Folmer, E. (2013). Formative evaluation in educational design
research. In T. Plomp & N. Nieveen (Eds.), Educational design research –
part A: An introduction (pp. 152-169). Enschede, the Netherlands: SLO.
Retrieved from https://slo.nl › publish › pages › educational-design-research-
part-a
Nightingale, D. & Cromby, J. (Eds.). (1999). Social constructionist psychology: A
crtitical analysis of theory and practice. Buckingham: Open University Press.
Nisbet, J. & Watt, J. (1984). Case study. In J. Bell, T. Bush, A. Fox, J. Goodey & S.
Goulding (Eds.). Conducting small-scale investigations in educational
management (pp. 79-92). London: Harper & Row, 79–92.
Nørgård, R.T., Mor, Y., & Bengtsen, S.S.E. (2019). Networked learning in, for, and
with the world. In Littlejohn, A., Jaidmark, J., Vrieling-Teunter, E., &
Nijland, F. (Eds.) (2019). Networked professional learning: Emerging and
equitable discourses for professional development (pp.71-88). Cham:
Springer. https://doi.org/10.1007/978-3-030-18030-0.
Norman, D. A. (1988). The psychology of everyday things. Basic Books: New York.
Norman, D. A. (1999). Affordance, conventions and design. Interactions, 6(3), 38-
42. https://doi.org/10.1145/301153.301168
Norton, A., & Cakitaki, B. (2016). Mapping Australian higher education 2016.
Grattan Institute. Retreived from https://grattan.edu.au/wpcontent/uploads-
/2016/08/875-Mapping-Australian-Higher-Education-2016.pdf
NSTC CoSTEM, National Science and Technology Council, Committee on STEM
Education. (2018). Charting a course for success: America’s strategy for
348 References
STEM education. Report, December 2018. Retrieved from:
https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-
Strategic-Plan-2018.pdf
Oblinger, D. (2013). Higher education and the connected age. EDUCAUSE Review
48(2), 4-5. Retrieved from https://er.educause.edu/articles/2013/4/higher-
education-in-the-connected-age
Office of the Chief Scientist. (2014). Benchmarking Australian Science, Technology,
Engineering and Mathematics. Australian Government, Canberra. Retrieved
from https://www.chiefscientist.gov.au/wpcontent/uploads/Benchmarking-
AustralianSTEM_Web_Nov2014.pdf
Office of the Chief Scientist. (2016). Australia’s STEM workforce: Science,
Technology, Engineering and Mathematics. Australian Governmnet,
Canberra. Retrieved from: file:///C:/Users/czaplins/Downloads/Sub044%20-
Attachment%20A.pdf
Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed
methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed
methods in social and behavioral research (pp. 351–383). Thousand Oaks,
CA: Sage.
Onwuegbuzie, A. J., Dickinson, W.B., Leech, N. L., & Annmarie G. Zoran, A.G.
(2009). A qualitative framework for collecting and analyzing data in focus
group research. International Journal of Qualitative Methods, 8(3), 1-21.
https://doi.org/10.1177/160940690900800301
Orlikowski, W. J. (2002). Knowing in practice: Enacting a collective capability in
distributed organizing. Organization Science, 13(3), 249–273. https://doi.-
org/10.1287/orsc.13.3.249.2776
References 349
O’Sullivan, G. (2011). The relationship between hope, eustress, self-efficacy, and
life satisfaction among undergraduates. Social Indicators Research, 101,
155–172. https://doi.org/10.1007/s11205-010-9662-z
O’Toole, L., Hayes, N., & Halpenny, A. M. (2020). Animating Systems: The
ecological value of Bronfenbrenner’s bioecological model of development. In
R. Barnett & N. Jackson (Eds.), Ecologies for learning and practice:
Emerging ideas, sightings and possibilities, (pp. 28-39). Oxon, New York:
Routledge. https://doi.org/10.4324/9781351020268
Owston, R., York, D. & Murtha, S. (2013). Student perceptions and achievement in a
university blended learning strategic initiative. Internet and Higher Education
18, 38-46. https://doi.org/10.1016/j.iheduc.2012.12.003
Palaganas, E. C., Sanchez, M. C., Molintas, M. P., & Caricativo, R. D. (2017).
Reflexivity in qualitative research: A journey of learning. The Qualitative
Report, 22(2), 426-438. Retrieved from https://nsuworks.nova.edu/tqr/vol22-
/iss2/5
Pane, J.F., Steiner, E.D., Baird, M.D. & Hamilton, L.S. (2015). Continued progress:
Promising evidence on personalised learning. Santa Monica, CA. Retreived
from https://www.rand.org/pubs/research_reports/RR1365.html
Pennebaker, J. W., Mehl, M. R., & Niederhoffer (2003). Psychological aspects of
natural language use: Our words, our selves. Annual Review of Psychology,
54, 547–577. https://doi.org/10.1146/annurev.psych.54.101601.145041
Perriton, L., & Reynolds, M. (2014). ‘Here be dragons’: Approaching difficult group
issues in networked learning. In Hodgson, V., De Laat, M., McConnell, D., &
Ryberg, T., (Eds.). (2014). The design, experience and practice of networked
learning (pp.109 – 126). New York: Springer. https://doi.org/10.1007/978-3-
319-01940-6_1
Perron, N. D. C. (2017). Bronfenbrenner’s ecological systems theory. In Killam, W.
K. P. N. C., & Degges-White, S. P. L. L. N. (Eds.), College student
350 References
development: applying theory to practice on the diverse campus, (pp. 197-
209). New York: Springer.
Piaget, J. (1952). The origins of intelligence in children. New York: International
Universities Press. Retrieved from https://www.pitt.edu/~strauss/origins-
_r.pdf
Piao, Y., & Ma, J. (2018). Teaching design strategy of innovation and
entrepreneurship course of college students. Proceedings of the
4th International Conference on Arts, Design and Contemporary Education
(ICADCE 2018). Retrieved from https://www.atlantisress.com/proceedings-
/icadce-18/25900351
Pintrich, P. R. (2000). Learning and motivation. In A. E. Kazdin (Ed.), Encyclopedia
of psychology Vol. 5 (pp. 22–26). Washington, DC: American Psychological
Association.
Plomp, T. (2013). Educational design research: An introduction. In T. Plomp and N.
Nieeven (Eds). Educational design research. Part A: An introduction, pp.10-
51. SLO: Netherlands. Retrieved from https://slo.nl › publish › pages ›
educational-design-research-part-a
Plomp, T. & Nieveen, N. (Eds). (2013). Educational design research. Part A: An
introduction. SLO: Netherlands. Retrieved from https://slo.nl › publish ›
pages › educational-design-research-part-a
Ponce, O., & Pagán-Maldonado N. A. (2015). Mixed methods research in education:
Capturing the complexity of the profession. International Journal of
Educational Excellence, 1, 111-135. https://doi.org/10.18562/ijee.2015.0005
Porter, W., W., Graham, C., R., Bodily, R., G., & Sandberg, D., S. (2016). A
qualitative analysis of institutional drivers and barriers to blended learning
adoption in higher education. Internet and Higher Education, 28, 17–27.
https://doi.org/10.1016/j.iheduc.2015.08.003
References 351
Powell, W.W., & Snellman, K. (2004). The knowledge economy. Annual Review of
Sociology. 30, 199-220. https://doi.org/10.1146/annurev.soc.29.010202.-
100037
Price, D. (2013). Open: How we’ll work, live and learn in the future. London: Crux
Publishing.
Prince, G. (2016). Research Reports 2015-16. In M. Trudinger and L. Watson (Eds.).
Australian Mathematical Sciences Institute. The University of Melbourne.
Retrieved from https://amsi.org.au/wp-content/uploads/2016/12/research-
report-web.pdf
PMSEIC, Prime Minister’s Science, Engineering and Innovation Council (2009,
December). Transforming learning and the transition of knowledge:
Preparing a learning society for the future. Commonwealth of Australia,
Canberra, Australia. Retrieved from http://www.chiefscientist.gov.au/-
wpcontent/uploads/Transforming-Learning-EWG-report-FINAL.pdf
Pugh, K. & Prusak, L. (2013). Designing effective knowledge networks [online].
MIT Sloan Management Review, 55(1), 79-89. Retrieved from:
http://www.aproaingenieria.com/intranet/uploads/designing-effective-
knowledge-networks.pdf
QCAA, Queensland Curriculum and Assessment Authority. (2015). 21st century
skills for senior education. An analysis of educational trends. November,
2015. Retrieved from https://www.qcaa.qld.edu.au/downloads/publications-
/paper_snr_21c_skills.pdf
QSA, Queensland Studies Authority (2014). Senior Syllabus. Mathematics C 2008
(amended 2014). Retrieved from: https://www.qcaa.qld.edu.au/downloads-
/senior/snr_maths_b_08_syll.pdf
352 References
Rajagopal, K., Verjans, S., Sloep, P. B., & Costa, C. (2012). People in personal
learning networks: Analysing their characteristics and identifying suitable
tools. In V. Hodgson, C. Jones, M. de Laat, D. McConnell, T. Ryberg, & P.
Sloep (Eds.), Proceedings of the eighth international conference on
networked learning 2012 (pp. 252-259). April, 2-4, 2012, Maastricht, The
Netherlands. Retrieved from https://dspace.ou.nl/bitstream/1820/4224/3/-
NLC2012_0054_paper_final.pdf
Rana, S., Ardichvili, A., & Polesello, D. (2016). Promoting self-directed learning in a
learning organization: tools and practices. European Journal of Training and
Development, 40(7), 470-489. https://doi.org/10.1108/EJTD-10-2015-0076
Reed, E. S. (1996). Encountering the world: toward an ecological psychology. New
York: Oxford University Press. https://doi.org/10.1093/acprof:oso/97801950-
73010.001.0001
Reeves, C., Herrington, J., & Oliver, R. (2005). Design research: A socially
responsible approach to instructional technology research in higher education.
Journal of Computing in Higher Education, 16(2), 97-116. https://doi.org-
/10.1007/BF02961476
Rienties, B., Hernandez Nanclares, N., Hommes, J., & Veermans, K. (2014).
Understanding emerging knowledge spill-overs in small-group learning
settings: A networked learning perspective. In Hodgson, V., De Laat, M.,
McConnell, D., & Ryberg, T., (Eds.). (2014). The design, experience and
practice of networked learning (pp.127-148). New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in the general theory of
planning. Policy Sciences, 4, 155–169. https://doi.org/10.1007/BF01405730
Robinson, J. (2012). Using focus groups. In S. Delamont (Ed.), Handbook of
qualitative research in education, (pp. 391-404). Elgar Publishing Limited:
Cheltenham. https://doi.org10.4337/9781849807296.00037
References 353
Rodrigues, J. (2018). Smartphones: fundamentally reshaping today’s teenagers.
Church, Communication and Culture, 3(1), 75-79. https://doi.org/10.1080-
/23753234.2018.1429223
Roediger, H.L., & Pyc, M. A. (2012). Inexpensive techniques to improve education:
Applying cognitive psychology to enhance educational practice. Journal of
Applied Research in Memory and Cognition, 1(4), 242-248. https://doi.org/-
10.1016/j.jarmac.2012.09.002
Roschelle, J. & Teasley, S. D. (1995). The construction of shared knowledge in
collaborative problem-solving. In C.E. O'Malley (Ed.), Computer-supported
collaborative learning (pp. 69–97). Berlin: Springer-Verlag. Retrieved from
http://umdperg.pbworks.com/f/RoschelleTeasley1995OCR.pdf
Rotherham, A. J., & Willingham, D.T. (2010). “21st-century” skills: Not new, but a
worthy challenge. American Educator, 17-20. Retrieved from https://www-
.aft.org/sites/default/files/periodicals/RotherhamWillingham.pdf
Roxå, T., & Mårtensson, K. (2015). Microcultures and informal learning: A heuristic
guiding analysis of conditions for informal learning in local higher education
workplaces. International Journal for Academic Development, 20(2), 193–
205. https://doi.org/10.1080/1360144X.2015.1029929
Roxå, T., Mårtensson, K., & Alveteg, M. (2011). Understanding and influencing
teaching and learning cultures at university: a network approach. Highner
Education, 62(1), 99–111. https://doi.org/10.1007/s10734-010-9368-9
Ryan, G. W., & Bernard, H. R. (2000). Data management and analysis methods. In
N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd
ed.) (pp. 769–802). Thousand Oaks, CA: Sage.
354 References
Ryan-Nicholls, K., & Will, C. (2009). Rigour in qualitative research: Mechanisms for
control. Nurse Researcher, 16, 70-82. https://doi.org/10.7748/nr2009-
.04.16.3.70.c6947
Ryberg, T., Sinclair, Ch., Bayne, S., & de Laat, M. (Eds.). (2016). Research,
boundaries, and policy in networked learning. Switzerland: Springer.
Research in Networked Learning. https://doi.org/10.1007/978-3-319-31130-2
Saks, K. & Leijen, Ä. (2014). Distinguishing self-directed and self-regulated learning
and measuring them in the e-learning context. Procedia – Social and
Behavioral Sciences, 112, 190-198. https://doi.org/10.1016/j.sbspro.-
2014.01.1155
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business
students, (5th ed.). Harlow: Pearson.
Schleicher, A., (2016, October 9). The case for 21st century learning [Web log post].
Retrieved from http://www.oecd.org/general/thecasefor21st-
centurylearning.html
Scholz, R.W., & Tietje, O. (2002). Embedded case study methods: Integrating
quantitative and qualitative knowledge. Thousand Oaks: California.
https://dx.doi.org/10.4135/9781412984027
Schreier, M. (2012). Qualitative content analysis in practice. Thousand Oaks, CA:
Sage.
Schreurs, B. (2014). Analaysing learning ties to stimulate continuous professional
development in the workplace. In Hodgson, V., De Laat, M., McConnell, D.,
& Ryberg, T., (Eds.). (2014). The design, experience and practice of
networked learning (pp. 207 – 224). New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
References 355
Schunk, D. H. (1989). Social cognitive theory and self-regulated learning. In B. J.
Zimmerman & D. H. Schunk (Ed.), Self-regulated learning and academic
achievement: Theory, research, and practice (pp. 83–110). New York, NY:
Springer Verlag. https://doi.org/10.1007/978-1-4612-3618-4_4
Schunk, D.H. (2012). Learning theories: An educational perspective. (6th ed.).
Pearson.
Schunk, D. H., & Greene, J.A. (2018). Historical, contemporary, and future
perspectives on self-regulated learning and performance. In D. H. Schunk &
J. A. Greene (Eds.), Handbook of self-regulation of learning and
performance (pp. 1-15). (2nd ed.). New York: Routledge, Taylor & Francis.
https://doi.org/10.4324/9781315697048
Schunk, D. H., & Swartz, C. W. (1993). Goals and progress feedback: Effects on
self-efficacy and writing achievement. Contemporary Educational
Psychology, 18, 337– 354. https://doi.org/10.1006/ceps.1993.1024
Schunk, D.H., & Zimmerman, B. J. (Eds.). (1998). Self-regulated learning: From
teaching to self-reflective practice. New York: Guilford Press.
Schunk, D. H., & Zimmerman, B. J. (Eds.). (2008). Motivation and self-regulated
learning: Theory, research, and applications, New York: Taylor & Francis.
https://doi.org/10.4324/9780203831076
Sefton-Green, J., Thomson, P., Jones, K., Bresler, L. (Eds). (2011). The Routledge
international handbook of creative learning. London: Routledge.
https://doi.org/10.4324/9780203817568
Sie, R. L.L., Pataraia, N., Boursinou, E., Rajagopal, K., Margaryan, A., Falconer, I.,
Bitter-Rijpkema, …, & Sloep, P. B. (2013). Goals, motivation for, and
outcomes of personal learning through networks: Results of a tweetstorm.
Educational Technology and Society, 16 (3), 59-75. Retrieved from
https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1685&context=asdpapers
356 References
Siemens, G. (2004). Connectivism: A learning theory for the digital age. [Web log
post]. elearnspace learning, networks, knowledge, technology, community.
Retrieved from http://www.elearnspace.org/Articles/connectivism.htm
Siemens, G. (2005a). Connectivism: A learning theory for the digital age.
International Journal of Instructional Technology & Distance Learning, 2(1),
3-11. Retrieved from Retrieved from http://www.itdl.org/
Siemens, G. (2005b). Connectivism: Learning as network-creation. [Web log post].
elearnspace learning, networks, knowledge, technology, community.
Retrieved from http://www.elearnspace.org/Articles-/networks.html
Siemens, G. (2006). Knowing knowledge. Retrieved from https://archive.org/details-
/KnowingKnowledge/page/n1
Siemens, G. (2007). PLEs – I acronym, therefore I exist. [Web log post].
elearnspace learning, networks, knowledge, technology, community.
Retrieved from http://www.elearnspace.org/blog/archives/002884.html
Siemens, G. (2010). What are learning analytics? elearnspace learning, networks,
knowledge, technology, community. Retreived from http://www.elearn-
space.org-/blog/2010/08/25/what-are-learning-analytics/
Siemens, G. (2019). I was wrong about networks. LinkedIn, March 26, 2019. [Web
log post] Retrieved from https://www.linkedin.com/pulse/i-wrong-networks-
george-siemens/
Siemens, G. (n.d.). Connectivism. A learning theory for the digital age. In R. E. West
Foundations of learnging and instructional design technology. Pressbook.
Retrieved from https://lidtfoundations.pressbooks.com/
Silverman, D. (2015). Interpreting qualitative data. Los Angeles, London, New
Delhi, Singapore, Washington DC: Sage.
References 357
Slaney, K. L., & Racine, T. P. (2011). On the ambiguity of concept use in
psychology: Is the concept “Concept” a useful concept? Journal of
Theoretical and Philosophical Psychology 31(2), 73-89. https://doi.org-
/10.1037/a0022077
Sloep, P. B., & Berlanga, A. J. (2011). Learning networks, networked learning.
Communicar, XIX(3), 55-63. http://dx.doi.org/10.3916/C37-2011-02-05
Sloep, P. B., Kester, L., Brouns, F., van Rosmalen, P., de Vries, F., de Croock, M., &
Koper, R. (2007). Ad hoc transcient communities to enhance social
interaction and spread tutor responsibilities. In Proceedings of the Sixth
Conference on IASTED International Conference Web-Based Education, vol.
2, 548-554. https://dl.acm.org/doi/abs/10.5555/1323159.1323256
Smith, S. R. (2013a). The connected learning environment in higher
education [Educause Web log posting]. Retrieved from
www.educause.edu/blogs/ssmith/connected-learning-environment-higher-
education
Smith, S. R. (2013b). The connected learning environment. EDUCAUSE brief.
Retrieved from https://library.educause.edu/resources/2013/7/the-connected-
learning-environment.
Snowden, M., & Halsall, J., P. (2016). Self-determined approach to learning: A
social science perspective. Cogent Education, 3, 2-9. Retrieved from
http://www.tandfonline.com/doi/pdf/10.1080/2331186X.2016.1247608?need
Access=true
Soderstrom, N. C., & Bjork, R. A.(2015). Learning versus performance: An
integrative review. Perspectives on Psychological Science, 10, 176–199.
http://dx.doi.org/10.1177/1745691615569000
358 References
Soderstrom, N. C., Yue, C. L., & Bjork, E. L. (2016). Metamemory and education. In
J. Dunlosky & S. K. Tauber (Eds.), Oxford library of psychology. The Oxford
handbook of metamemory (pp. 197-215). New York, NY, US: Oxford
University Press. https://doi.org/10.1093/oxfordhb/9780199336746.001.0001
Sommerhoff, D., Szameitat, Freydis Vogel, A. F., Chernikova, O., Loderer, K. &
Fischer, F. (2018). What do we teach when we teach the learning sciences? A
document analysis of 75 graduate programs. Journal of the Learning
Sciences, 27, 319–351. https://doi.org/10.1080/10508406.2018.1440353
Stiles, R.J. & Wicox, K. (2016). Blending human intelligence and analytics for
student success. EDUCAUSE Learning Initiative. Retrieved from
https://library.educause.edu/resources/2016/8/blending-human-intelligence-
and-analytics-for-student-success.
Strauss, A. & Corbin, J. (1994). Grounded theory methodology. In NK Denzin & YS
Lincoln (Eds.), Handbook of qualitative research (pp. 217-285). Thousand
Oaks, Sage Publications.
Sturman, A. (1999) Case study methods. In J. P. Keeves and G. Lakomski (Eds.),
Issues in ducational research (pp.103-112) Oxford: Elsevier Science.
Swanson, R.A. and Holton, E.F. (2009). Foundations of human resource
development, (2nd ed.), Berrett-Koehler, San Francisco, CA. retrieved from
https://pdfs.semanticscholar.org/a1e8/79c1e63a10eb8906391daaa91575947d
5090.pdf
Sweller, J., (1988). Cognitive load during problem solving: effects on learning.
Cognitive Science, 12(2), 257-285. https://doi.org/10.1016/0364-
0213(88)90023-7
Taminiau, E.M.C., Kester, L., Corbalan, G., Spector, J.M., Kirschner, P.A., & van
Merriënboer, J.J.G. (2015). Designing on-demand education for simultaneous
References 359
development of domain-specific and self-directed learning skills. Journal of
Computer Assisted Learning, 31, 405–421. https://doi.org/10.1111/jcal.12076
Timmis, S., & Williams, J. (2016). Transitioning across networked, workplace and
educational boundaries: Shifting identities and chronoptic movements. In
Ryberg, T., Sinclair, Ch., Bayne, S., & de Laat, M. (Eds.). (2016). Research,
boundaries, and policy in networked learning (pp.111- 126). Switzerland:
Springer. Research in Networked Learning. https://doi.org/10.1007/978-3-
319-31130-2
Tan, P-L. T., Caleon, I. S., Jonathan, C. R., and Koh, E. (2014). A dialogic
framework for assessing collective creativity in computer-supported
collaborative problem-solving tasks. Research and Practice in Technology
Enhanced Learning, 9(3), 411–437.
Tashakkori, A., & Creswell, J. W. (2007). The new era of mixed methods.
[Editorial]. Journal of Mixed Methods Research, 1(1), 3-7. https://doi.org/10-
.1177/2345678906293042
Thibaut, P. & Carvalho, L. (2018). Designing for youth engagement across formal
and informal learning networks. In B. Dohn, M. de Laat, P.
(Eds.). Proceedings of the 11th International
Conference on Networked Learning 2018, (pp. 149-156). Retrieved from
http://www.networkedlearningconference.org.uk/abstracts/papers/carvalho_1
3.pdf
The New York Academy of Sciences. (2017). STEM education in Asia Pacific.
Research reveals students' interest in STEM is strong in the region, but
students and teachers need more resources. Retrieved from:
https://www.nyas.org/press-releases/stem-education-in-asia-pacific-study-
reveals-strong-student-interest-in-stem-but-students-and-teachers-need-more-
resources/
360 References
Tolmie, A., & Boyle, J. (2000). Factors influencing the success of computer-
mediated communication (CMC) environments in university teaching: A
review and case study. Computers & Education, 34(2), 119–140.
Tricot, A., & Sweller, J. (2014). Domain-specific knowledge and why teaching
generic skills does not work. Educational Psychology Review, 26(2), 265-
283. https://doi.org/10.1007/s10648-013-9243-1
Twenge, J.M. (2017). iGen: Why today’s super-connected kids are growing up less
rebellious, more tolerant, less happy and completely unprepared for
adulthood? (And what that means for the rest of us?). New York: ATRIA
Books.
Usher, E.L., & Schunk, D.H. (2018). Social cognitive theoretical perspective of self-
regulation. In D.H Schunk & J.A. Greene (Eds.), Handbook of self-regulation
of learning and performance (pp. 19-35). Second Edition, New York:
Routledge, Taylor & Francis. https://doi.org/10.4324/9781315697048
University College London (UCL) [Website] (n. d.). https://www.ucl.ac.uk/teaching-
learning/connected-curriculum-framework-research-based-education.
Üulüsoy A., de Haan, M., & Leander, K. (2014). Learning through network
interaction: The potential of ego-networks. In Hodgson, V., De Laat, M.,
McConnell, D., & Ryberg, T., (Eds.). (2014). The design, experience and
practice of networked learning (pp. 225 – 240). New York: Springer.
https://doi.org/10.1007/978-3-319-01940-6_1
Van den Akker, J. (1999). Principles and methods of development research. In J. van
den Akker, R. Branch, K. Gustafson, N. Nieveen, & T. Plomp (Eds.), Design
approaches and tools in education and training (pp. 1-14). Dordrecht:
Kluwer Academic Publishers.
References 361
Van den Akker, J., Gravemeijer, K., McKenney, S., & Nieveen, N. (Eds.). (2006).
Educational design research. London: Routledge.
https://doi.org/10.4324/9780203088364
Van Merriënboer, J. J. G. & Kirschner, P. A. (2017). Ten steps to complex learning.
A systematic approach to four-component instructional design. New York:
Routledge. https://doi.org/10.4324/9781315113210
Van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex
learning: recent developments and future directions. Educational Psychology
Review, 17(2), 147-177. https://doi.org/10.1007/s10648-005-3951-0
Van Meeuwen, L. W., Brand-Gruwel, S., Kirschner, P. A., De Bock, J. J. P. R.,
Oprins, E., & Van Merriënboer, J. J. G. (2013). Self-directed learning in
adaptive training systems: A plea for shared control. Technology, Instruction,
Cognition and Learning, 9, 193-215.
Verhagen, P. (2006). Connectivism: a new learning theory? [Web log post].
Retrieved from https://www.scribd.com/doc/88324962/Connectivism-a-New-
Learning-Theory.
Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international
frameworks for 21st century competences: implications for national
curriculum policies. Journal of Curriculum Studies, 44(3), 299-321.
https://doi.org/10.1080/00220272.2012.668938
Vygotsky, L. (1962). Thought and language. Cambridge, MA: MIT Press.
Vygotsky, L.S. (1978). Mind in society: The development of higher psychological
processes. Cambridge, Massachusetts: Harvard University Press.
Wagner, L. & Lakusta, L. (2009). Using language to navigate the human mind.
Perspectives on Psychological Science, 4(2), 177-184. https://doi.org/10.-
1111/j.1745-6924.2009.01117.x
362 References
Walker, G. (2013). A cognitive approach to threshold concepts. Higher Education,
65, 247-263. https://doi.org/10.1007/s10734-012-9541-4
Walker, R. (2014). Blended problem-based learning: designing collaboration
opportunities for unguided group research through the use of Web 2.0 tools.
In Hodgson, V., De Laat, M., McConnell, D., & Ryberg, T., (Eds.). (2014).
The design, experience and practice of networked learning (pp.165-184).
New York: Springer. https://doi.org/10.1007/978-3-319-01940-6_1
Watty, K., McKay, J., & Ngo, L. (2014). CPA Australia global research perspectives
program. Innovative teaching, learning and assessment in accounting
education: Engaging with digital technologies that enhance student learning.
Final Report, September 2014, Deakin University, Melbourne, Australia.
Retrieved from http://www.deakin.edu.au/data/assets/pdf_file/0007/-
296413/cpareport.pdf
Westwell, P. (n. d). Learning for life in the 21st century. Flinders centre for Science
Education in the 21st Century. Retrieved from http://www.stclareof-
assisiconder.com/uploads/5/3/3/4/53344669/learning_for_life_in_the_21st_ce
ntury_v2.pdf
Werquin, P. (2007). Recognising non-formal and informal learning: outcomes,
policies and practices. OECD. Retrieved from http://www.oecd.-
org/edu/skills-beyond-school/recognitionofnon-formalandinformallearning-
home.htm
Wigfield, A., Klauda, S.L., & Cambria, J. (2011). Influences on the development of
academic self-regulatory processes. In B. J. Zimmerman & D. H. Schunk
(Eds.), Handbook of self-regulation of learning and performance (pp.33-48).
New York: Routledge. https://doi.org/10.4324/9781315697048
Wilson, N., & Mclean, S. (1994). Questionnaire design: A practical introduction,
Newtown Abbey, Co. Antrim: University of Ulster Press.
References 363
Williamson, B. (2016). Boundary brokers: mobile policy networks, database
pedagogies, and algorithmic governance in Education. In Ryberg, T., Sinclair,
Ch., Bayne, S., & de Laat, M. (Eds.). (2016). Research, boundaries, and
policy in networked learning (pp.41-58). Switzerland: Springer. Research in
Networked Learning. https://doi.org/10.1007/978-3-319-31130-2
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J.
Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational
theory and practice (pp.279-306). Hillsdale, NJ: Erlbaum.
Winne, P. H., & Jamieson-Noel, D. (2002). Exploring students’ calibration of self-
reports about study tactics and achievement. Contemporary Educational
Psychology, 27, 551–572. https://doi.org/10.1016/S0361-476X(02)00006-1
Yin, R.K. (2003). Case study research: Design and methods (3rd ed.). Newbury Park,
CA: Sage.
Yin, R. K. (2013). Validity and generalization in future case study evaluations.
Evaluation, 19(3), 321-332. https://doi.org/10.1177/1356389013497081
Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Thousand
Oaks, CA: SAGE.
Zeidner, M. (2019). Self-regulated learning: Current fissures, challenges, and
directions for future research. High Ability Studies, 1-22. https://doi.org/10.-
1080/13598139.2019.1584034
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory
into Practice, 41(2), College of Education, The Ohio State University.
https://doi.org/10.1207/s15430421tip4102_2
Zimmerman, B. J. (2008). Goal setting: A key proactive source of academic self-
regulation. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-
364 References
regulated learning: Theory, research, and applications (pp. 267–295). New
York: Erlbaum.
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011), Handbook of self-regulation of
learning and performance. New York: Routledge. https://doi.org/10.4324-
/9781315697048