phd defence: learner models in online personalized educational experiences: an infrastructure and...
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FULL TEXT freely available at http://doc.rero.ch/record/210109 Technologies are changing the world around us, and education is not immune from its influence: the field of teaching and learning supported by the use of Information and Communication Technologies (ICTs), also known as Technology Enhanced Learning (TEL), has witnessed a huge expansion in recent years. This wide adoption happened thanks to the massive diffusion of broadband connections and to the pervasive needs for education, highly connected to the evolution in sciences and technologies. Therefore, it has pushed up the usage of online education (distance and blended methodologies for educational experiences) to, even in lately years, unexpected rates. Alongside with the well known potentialities, digital-based educational tools come with a number of downsides, such as possible disengagement on the part of the learner, absence of the social pressures that normally exist in a classroom environment, difficulty or even inability from the learners to self-regulate and, last but not least, depletion of the stimulus to actively participate and cooperate with lectures and peers. These difficulties impact the teaching process and the outcomes of the educational experience (i.e. learning process), being a serious limit and questioning the broader applicability of TEL solutions. To overcome these issues, there is a need of tools to support the learning process. In the literature, one of the known approach to improve the situation is to rely on a user profile, that collects data during the use of the eLearning platforms or tool. The created profile can be used to adapt the behaviour and the contents proposed to the learner. On top of this model, some researches stressed the positive effects stimulated by the disclosure of the model itself for inspection purposes by the learner. This disclosed model is known as Open Learner Model (OLM). The idea of opening learners' profile and eventually integrate them with external on-line resources is not new and it has the ultimate goal of creating global and long-run indicators of the learner's profile. Also the representation aspect of the learner model plays a role, moving from the more traditional approach based on the textual and analytic/extensive representation to the graphical indicators that are able to summarise and to present one or more of the model characteristics in a way that is considered more effective and natural for the user consumption. Relying on the same learner models, and stressing the different aggregation and representation capabilities, it is possible to either support self-reflection of the learner or to foster the tutoring process to allow proper supervision by the tutor/teacher. Both the objectives can be reached through the graphical representation of the relevant information, presented in different ways. ... CONTINUES ...TRANSCRIPT
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Presented by L. Mazzola
Faculty of Communication SciencesInstitute for Communication Technologies
University of Lugano, CH
Lugano - 23 May 2014
Learner Models in Online Personalized Educational Experiences: an infrastructure and
some experiments
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Agenda● The context
● The problem
● A solution
● The proposal
● Initial analysis in GRAPPLE
● Some testing in and outside GRAPPLE
● Consideration/Conclusions
● Possible next steps
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The context● Technology Enhanced Learning (TEL)
– ICT applied to education process● Availability of connection● Enhancement in research and science → new knowledge● Support for individual needs● Availability of Learning Management and Intelligent Tutoring System
– Possibility of continuous education● Distance and Blended modalities● Informal learning● On-the-job training
→ additional resources and tools to support educational experiences
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The problem of TEL● PROS:
– Decoupling of time and space– Personal pace– Asynchronous interaction
● CONS:
– Disengagement / Drop Out– Less “social pressure”– Difficulty in self-regulating– Depletion of stimulus to active participation
● Needs of tools to support the learning/teaching process
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A solution, in the literature● Creating a user profile
– Adoption of content and presentation– Positive effect of Disclosure (OLM)– Integration with other sources/external provider (global
and long-run indicators)– Representation aspect (Information Visualization):
● From text/analytic to graphical/summary
● For Supporting purposes:
– Enhance and stimulating self-reflection / awareness– Fostering the tutoring process
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AIMS
(1) Representation aspects:
– How the OLM can be represented fruitfully to learners?– ...and to teacher/tutors?
(2) Adaptive and social visualization of OLM:
– How they can affect the user experience?
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OBJECTIVES
(1) Demonstrate that mixing different and heterogeneous sources can have a meaningful didactic interpretation
(2) Explore approaches and representational models considered effective by learners and tutors/teachers
(3) Measure the perceived effect/impact of the introduction of such a tool
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The proposal: GVIS
Configurations / Semantics
Data Sources
Processing levels
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The proposal : GVIS● PHP code with OO approach
● 3 layers that are specialized in source interfacing, aggregation of data into information, and presentation aspects
● Each layer controlled by one or more XML description of the operation/attribute (didactic semantic)
● AJAX controlled interaction (interactive and responsive)
● Adaptive segments in the XML configuration
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The proposal: GVIS
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UML sequence
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Representation aspect analysisOn mockups, through online questionnaire (learner & teacher)
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Analysis of MockUp● # Users:
– 43 Learners– 32 Instructor (Tutor/Teacher)
● Results:
– Simpler visualizations preferred– More complex on user request (exploration)– Usefulness of filtering capabilities of data presented– Peers comparisons useful, but only at aggregated level– Didactic meaningful aggregation for tutors/teacher
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Widgets for GRAPPLE
Bridge
Bridge
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Subjective Assessment of VisualisationDimensions Learner Teacher/Tutor
Perceived usability/suitability - in terms of:
- suitability for the task XX XX
- self-descriptiveness XX XX
Visualization benefits:
- Meta-cognition XX XX
- Cognitive load XX XX
- Learning effectiveness XX
- Benefits for instructors (personalised/individualised instruction) XX
- Benefits for peers/collaboration XX
- Acceptance XX
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Subjective Assessment of Visualisation: result(+) Suitable for their intended purpose and largely self-descriptive and understandable
(+) Suitable for getting an overview of the current status in the learning process
(+) Generally easy to understand and not unnecessarily complex
(-) Comparison with the class might be problematic and negatively affect self-worth and collaboration, especially for underachievers
● Better a comparison with one self own prior performance
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GVIS and Moodle
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GVIS in Moodle: evaluationQuestion ++ + 0 -Easy understandable X
Not unnecessarily complex X
Help instructor to tailor to individual needs X
Suitable for getting an overview of the current status X
Visualization does not provide irrelevant information X
Visualization can help learners to reflect on their learning X
Usefulness of comparison with other peers for reflection X
Expected impact on learners performances X
Promote awareness and understanding of learning progress X
Help teacher in better understand the learners needs X
Visualization able to leverage mental workload X
Risk of hindering the collaboration amongst peers X
Additional cognitive effort on learner to understand it X
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GVIS and Adapt2: social visualization
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GVIS and Adapt2: social visualization
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GVIS and Adapt2: evaluationMidTerm Final
WITH GVIS
WITHOUT
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GVIS for User navigation history
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GVIS for Domain profile
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Adaption of visualizations● At two levels: aggregation and building (presentation)
<cond> + <op>(v1 AND ((A > 3) OR !(z)))</op> // FIRST LEVEL | <operands> | <val id="v1">CourseX.Concepts.list</val> | <val id="z">CourseX.Student.count</val> | <val id="A">CourseX.ConceptA.mean.knowledge</val> | </operands> + <true>...</true> + <false> | + <op>(h < t)</op> // SECOND LEVEL CONDITION | | <operands> | | <val id="t">CourseX.ConceptA.mean.knowledge</val> | | <val id="h">CourseX.ConceptA.userH.knowledge</val> | | </operands> | + <true>...</true> | + <false>...</false> +</false> </cond>
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Adaption of visualizations: examplesGraphical format & aggregation
Graphical vs. Textual
Relative vs. Absolute scale
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Results
● Found an impact on user behaviors, enhanced by social aspects
● Simpler and immediate presentation correlate with higher (perceived or measured) effects
● Positive social pressure factor for learners, improved by the peers comparison functionality
– Sense of community – Stimulating healthy competition
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Results
● Tutors/teacher: preferred compact, intuitive, and just-in-time information (didactic interpretation)
– Clearer picture– Able to support identifying performances issues
● Possible cognitive overload: needed further studies.
● Sum-up: consider generally useful and enough flexible to be adapted to different needs and context.
● TinCan API recently solved some of these issues...
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Possible Next Steps● A graphical language to specify the pipeline from data
to didactic meaningful information
● An interface/editor for generating the XML configurations of extractor, aggregator and builder from the graphical language
● A library of freely available basic didactic components (common and useful configurations) for reuse
● a set of adaptation templates could simplify the usage of these capabilities by the Instruction Designers
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Possible Next Steps● More filtering and data reordering procedures
through an easy visual interface to facilitate the exploratory navigation of the information
● A more extensive and structured testing of the tool, both to understand
– its full potentialities and threats – to analyse more in depth the impact that a visualisation
(in all its form: adaptive, social and others) can have on different type of education models, from blended courses to completely online ones or from single course to fully online degree.
