what are we learning from learning analytics: rhetoric to reality escalate 2014
DESCRIPTION
discussion of models of learning analytics implementationTRANSCRIPT
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• Student from Shanghai-based East China Normal University
• "Last month, you spent less on meals. Are you in financial difficulty? If so, please contact me via phone, text message or e-mail.“
Introduction
http://www.bjreview.com.cn/nation/txt/2014-06/23/content_625466.htm
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• Automatically track students' meal card spending.
• If spending falls under a threshold level, a designated faculty member sends the student a short message to check whether they are in financial difficulty.
Introduction
http://www.bjreview.com.cn/nation/txt/2014-06/23/content_625466.htm
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• Highlights the rapidly growing list of applications of student data
• Academic
• Social
• Pastoral
Introduction
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This talk:
• What are we learning from the implementation of LA into HE?
• What are the conversations, expectations and reactions to this nascent field?
• What are the emerging models for institutional implementation?
Introduction
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Does the rhetoric of LA meet the reality?
Introduction
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• Why the interest in LA now?
Drivers
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• 1926 - Pressey built an instructional machine to provide multiple choice questions
• “…with the addition of a simple attachment the apparatus will present the subject with a piece of candy or other reward upon his making on any given score for which the experimenter may have set the device…”
Drivers
Shute, V. J., & Psotka, J. (1994). Intelligent Tutoring Systems: Past, Present, and Future (No. AL/HR-TP-1994-0005). ARMSTRONG LAB BROOKS AFB TX HUMAN RESOURCES DIRECTORATE.
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• Scale, access and application• Ease of access to learner data – LMS, SIS, mobile• Growth in adoption of technical devices• Huge investment in analytics – industry &
Government
Data
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• Learning Analytics • “game changer” for education
Learning Analytics
…is the collection, collation, analysis and reportingof data about learners and their contexts, for the purposes of understanding and optimizing learning
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• One perspective - Industry
Industry rhetoric
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Industry rhetoric
“Get answers to your most important questions like:• How can I easily find students who are at-risk?
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Industry rhetoric
“Get answers to your most important questions like:• How can I easily find students who are at-risk?
• Yes possible – much research in this area • However, ignores the complexity• Context is critical• Not all courses are alike – student diversity
and approach
Overstated
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Industry rhetoric
“Get answers to your most important questions like:• Who are the most innovative instructors?”
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Industry rhetoric
“Get answers to your most important questions like:• Who are the most innovative instructors?”
• How and why? What defines innovative in this space given the myriad of tools and learning approaches available
Why?
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Industry rhetoric
“…In five years the classroom will learn you! And personalize course work accordingly”
http://www.research.ibm.com/cognitive-computing/machine-learning-applications/decision-support-
education.shtml#fbid=MRUeQg4jzVG
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Industry rhetoric
“…In five years the classroom will learn you! And personalize course work accordingly”
• Currently available if:• Cognitive tutor, Knewton, Knowillage
• Ryan Baker – on/off task behaviour; gaming and choice of major
Plausible
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Industry rhetoric
“Enhance student outcomes with the ability to monitor, evaluate, and predict learner performance to drive retention and improve outcomes.”
• Much work in this area to predict performance however, intervention strategies less well understood.
• Greater recognition SRL
http://www.brightspace.com/solutions/higher-education/advanced-analytics/
Available but not utilised
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Industry rhetoric
“…predictive analytics capabilities help educators target learning strategies and pre-emptively mentor at-risk learners.”
http://www.brightspace.com/solutions/higher-education/advanced-analytics/https://www.flickr.com/photos/tadeeej/3228729514/
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Industry rhetoric
Do we need predictive analytics here?
https://www.flickr.com/photos/tadeeej/3228729514/
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Industry rhetoric
• Unlikely – practice is difficult change. However first step is to aid identification.
• Tannes et al (2011) - Course Signals feedback• Instructors – feedback was motivational• Student success related to instructional
feedback
Tannes, et al (2011) . Using Signals for appropriate feedback. Perceptions and practice. Computers and Education, 57, (4), 2414 - 2422
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Industry offer solutions to problems
We still need to identify the problem.
Industry rhetoric
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What is missing: a focus on learning process
• SRL proficiency (Gasevic; Winne)• Discourse analysis and text mining (Rose)• Learning design and Instructional conditions
(Lockyer; Gasevic)• Learning dispositions (Deakin Crick, Buckingham
Shum)• Literacies or fluencies (Siemens)• Creativity (Pei Ling Tan)
Research rhetoric
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Great research BUT:
• Tends to ignore the complexity of university wide practice
• Predominantly, small scale and technology and institutional specific
• Lacks guidance to aid further adoption• Frequently requires high level skills and capacities
Research rhetoric
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Hence:• Very few university wide examples of LA adoption
• But obviously an area of increasing need and importance
Research rhetoric
Leads to questions related to how to
implement, get started and what data?
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National project to benchmark LA status, policy
and practices for Australian Universities
Learning Analytics
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Interviews with 39 Universities and 30 “experts”:
• Identification of current practice, methods and approaches
• Identification of key drivers for institutions, stage of development, process for implementation, project leads
Benchmarking
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Research perspective:
• Focus on understanding learning processes• Broad range of data sets –larger size and range
of data (relational data) • Limited interest in the scalability of findings
across institution (at least not a stated intention)
Benchmarking
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Research perspective:
“My hope [for LA] is that we can develop a better theory about how people learn and forge recommendations that might nudge learners toward more productive, more efficient, more satisfying ways of learning”
Benchmarking
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University leaders perspective:
• Primarily focused on retention• “It’s [LA] a tool for improving retention”• Limited mention of LA as a means to improve
learning• Main driver is budget (cost savings)• Perception that it is only related to – LMS and
SIS• Limited number of data sets considered
Benchmarking
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University leaders perspective:
• Success is seen as staff access to information• Limited understanding of the application of
interventions that are data informed• Data visualisations – dashboard development is
the endpoint and goal• Few institutions with stated LA policy and strategy
Benchmarking
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• Widening gap between University Admin and researchers
• Admin – Industry very similar
Benchmarking
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Reality is sobering:
• Need to develop greater understanding of the role of technology and role of data in an institution
• Access to data does not mean change in practice
• Interventions and early alerts must be constantly evaluated, revised and contextualised
Reality
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2005 – Goldstein & Katz:
• Stage 1: Extraction and reporting of transaction-level data
• Stage 2: Analysis and monitoring of operational performance
• Stage 3: “What-if” decision support (such as scenario building)
• Stage 4: Predictive modeling & simulation• Stage 5: Automatic triggers and alerts
(interventions)
Reality
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2005 – Goldstein & Katz:
• Stage 1: Extraction and reporting of transaction-level data
• Stage 2: Analysis and monitoring of operational performance
• Stage 3: “What-if” decision support (such as scenario building)
• Stage 4: Predictive modeling & simulation• Stage 5: Automatic triggers and alerts
(interventions)
Reality
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• Yanosky (2009) – 305 institutions, 58% at stage 1, 20% at stage 2
• Bichsel (2012)• Interest in analytics is high, but many
institutions had yet to make progress beyond basic reporting.
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2014 LA organisational adoption is low:
• Australia is predominantly at a stage of basic reporting
• Very few institutions have an enterprise approach
• While the research has well progressed -implementation remains a challenge.
Reality
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• Essentially, 2 models emerging1. Solutions focused
• IT driven or• L&T driven or• Industry
2. Process focused• Individual “faculty” or• Networked and integrated
Reality
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Reality
Adaptability of system to meet org needs
Low High
Low
High
Ease of adoption
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Reality
Adaptability of system to meet org needs
Low High
Low
High
Ease of adoption
Solutions
focused
Process
focused
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Reality
Adaptability of system to meet org needs
Low High
Low
High
Ease of adoption
Solutions
focused
Process
focused
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Reality
Adaptability of system to meet org needs
Low High
Low
High
Long term impact
Solutions
focused
Process
focused
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Solutions focused – Short term gains
Reality
Advantages Disadvantages
• Cost • Locked in
• Speed of delivery • Short time for acceptance
• Ease of dissemination
• Lacks capacity building
• Scalable, risk mitigation
• Access to data is often limited
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Process focused – Longer term gains
Reality
Advantages Disadvantages
• Capacity building • Time required
• Adaptive to changing reqs
• Sustained leadership and principles of access
• Acceptance ofprocess
• Complexity
• Shared ownership • Raises org threat
• Evidenced based
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Common model – Solutions focused:
• IT lead and implemented• Closed system focused on scalability,
performance, and list of features • Dashboards/ reports are important• Dissemination and access gains
[Success is seen as staff access to information]
• Where is the why?
Reality
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Conclusion
LA sophistication modelSiemens, G., Dawson, S., & Lynch, G. (2013). Improving the Productivity of the Higher Education Sector: Policy and Strategy for Systems-Level Deployment of Learning Analytics. Society for Learning Analytics Research for the Australian Government Office for Learning and Teaching.
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Conclusion
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Conclusion
Solutions focusedLimited view of LA – eg retention
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Is there an alternative:
• What are the organisational needs and how to gain both impact and adoption
• How do we merge both models to gain both short and long term impact?
Reality
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Developing models:
• Cross organisation• IT, L&T, Faculty, Research, Administrators
• Development of exemplars and research informed.
• Process is future looking and agile• Increased time required for acceptance and
discussion• Problem focused – understand the problem
An alternative
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Developing models:
• Building organisational capacity• Time for organisational acceptance• Identify sites of interest and growth• Research ideas promoted and faculty invited
into new spaces• Need to act on data and findings
An alternative
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Complex adaptive system:
• Education is complex• Learning is complex• Organisations are complex
• CAS are systems large numbers of agents that interact and adapt or learn
• Non-linear and resilient
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Complex Leadership Theory:
• CAS – requires new forms of leadership (Complex leadership theory - Uhl-Bien et al)
• Interactive, engaged, multi-level and contextual
• Takes advantage of the dynamic capabilities the system
• Leadership vs leaders
Uhl-Bien, M., Marion, R. & McKelvey, B. (2007). Complexity Leadership Theory: Shifting leadership from the industrial age to the knowledge era, The Leadership Quarterly, Volume 18(4),298-318
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Complexity Leadership:
Administrative Leadership
Adaptive Leadership
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Complexity Leadership:
Administrative Leadership
Adaptive Leadership
Administrative stifles
adaptive. (Bureaucratic
and top down)
However – it is driven
and solution focused
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Complexity Leadership:
Administrative Leadership
Adaptive Leadership
Adaptive (lack of
integration)
However capacity
building and
innovation focused
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Complexity Leadership:
Administrative Leadership
Adaptive Leadership
Balanced
Capacity building,
innovative
responses to
complex problems
Enabling
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Enabling:
• Leadership- focused on process and enabling staff
• Developing awareness and building capacity
• Diverse teams represented
• IT/ L&T – systems
• Data analysts
• Data wranglers
• Teaching staff
• ResearchersE.g.
• Open UK
• University of Michigan
• University of Texas
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Conclusion
Process focusedBroad view of LA
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Conclusion
• Change in education is complex and multi-faceted• Requires new models for implementation and
leadership• Enabling leadership• models that are agile and research informed
• Requires an inter-disciplinary approach• Embrace Friction - generates discussion and
innovation
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Conclusion
For the reality of LA to meet the rhetoric (to reach potential):
• LA is not a technology
• LA is not a dashboard
• LA is not one individual
• LA is team based
• LA is dynamic and requires longer term
investment and process