connecting analytics, theory and the organisation: a model for learning analytics in higher...
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DIVISION OF STUDENT LEARNING
Connecting Analytics, Theory and
the Organisation:
Exploring a multi-dimensional
model of learning analyticsSimon Welsh (Senior Learning Analytics Officer): [email protected]
Assoc Prof Philip Uys (Director, Learning Technologies): [email protected]
Presentation at ASCILITE 2014
Nov 2014 Slides available from: http://www.slideshare.net/puys/connecting-
analytics-theory-and-the-organisation
DIVISION OF STUDENT LEARNING
Acknowledgements
Learning Analytics Working Party, CSU
Assoc Prof Philip Uys (Chair)
Paul Bristow
Dr Andrea Crampton
Dr Nick Drengenberg
Pat Loria
Vicki Pitcher
Liz Smith
Simon Welsh
Prof Alan Bain
Nina Clemson
Prof Barney Dalgarno
Dr Yann Guisard
Julie Newham
Tim Scott
Assoc Prof Joy Wallace
Dr Janelle Wheat
DIVISION OF STUDENT LEARNING
Overview
A. Why connect analytics, theory and the organisation?
B. Unpacking the CSU Learning Analytics Model, including
group discussions
DIVISION OF STUDENT LEARNING
A. Why connect analytics, theory and the
organisation?
Learning analytics only exists to help us adapt learning and
teaching to enhance student success
Making adaptations in learning and teaching requires
understanding of how students learn, their learning dispositions
and the learning context
Theoretical assumptions about these elements need to be
explicitly built into our analytics ... or other assumptions will be
implicitly built in: theory defines what we need to measure
To drive adaptation, learning analytics must be “plugged into”
people (and systems) who have the capacity, desire and agency
to use them: organisational dynamics define how we use
analytics
DIVISION OF STUDENT LEARNING
A. Why connect analytics, theory and the
organisation?
Questions?
DIVISION OF STUDENT LEARNING
Student
Success:• Quality
learning
• Achievement
of goals
• Retention &
progress
• Wellbeing
Focussed on student success
B. Unpacking the CSU Learning Analytics Model
DIVISION OF STUDENT LEARNING
Subject
Level
Course
Level
University
Level
Student Learning Characteristics
Student Learning Behaviours
Teaching
Curriculum Design
Learning Environment
Support
Drivers of Student Success
Student
Success:• Quality
learning
• Achievement
of goals
• Retention &
progress
• Wellbeing
Six domains and
three levels
B. Unpacking the CSU Learning Analytics Model
DIVISION OF STUDENT LEARNING
Strategic Techno-
logical
Structural Operational Cultural
Subject
Level
Course
Level
University
Level
Student Learning Characteristics
Student Learning Behaviours
Teaching
Curriculum Design
Learning Environment
Support
Org Design Metrics
and
Methods
Agents
Drivers of Student Success
Emergent
Feedback
and
Reporting
Student
Success:• Quality
learning
• Achievement
of goals
• Retention &
progress
• Wellbeing
Organisational Dynamics
B. Unpacking the CSU Learning Analytics Model
DIVISION OF STUDENT LEARNING
Strategic Techno-
logical
Structural Operational Cultural
Subject
Level
Course
Level
University
Level
Student Learning Characteristics
Student Learning Behaviours
Teaching
Curriculum Design
Learning Environment
Support
Org Design Metrics
and
Methods
Agents
Drivers of Student Success
Intervention, Adaptation and
Evaluation
Emergent
Feedback
and
Reporting
Student
Success:• Quality
learning
• Achievement
of goals
• Retention &
progress
• Wellbeing
Organisational Dynamics
B. Unpacking the CSU Learning Analytics Model
DIVISION OF STUDENT LEARNING
B. Unpacking the CSU Learning Analytics Model
Questions?
DIVISION OF STUDENT LEARNING
References
Bain, A., & Parkes, R. J. (2006). Can Schools Realise the Learning Potential of Knowledge
Management? Canadian Journal of Learning and Technology, 32(2).
Diaz, V., & Fowler, S. (2012). Leadership and Learning Analytics. Educause Learning Initiative
Brief, November 2012, pp. 1-4. Retrieved from:
http://www.educause.edu/library/resources/leadership-and-learning-analytics
Ferguson, R., (2012). Learning analytics: drivers, developments and challenges. International
Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317.
Long, P & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education.
Educause Review, September/October 2011, pp. 31-40. Retrieved from:
http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education
Tynan, B., & Buckingham Shum, S. (2013). Designing Systemic Learning Analytics at the
Open University. Retrieved from: http://www.slideshare.net/sbs/designing-systemic-
learning-analytics-at-the-open-university
DIVISION OF STUDENT LEARNING
Thank You
Simon Welsh (Senior Learning Analytics Officer): [email protected]
Assoc Prof Philip Uys (Director, Learning Technologies): [email protected]
Nov 2014
Slides available from: http://www.slideshare.net/puys/connecting-analytics-theory-and-
the-organisation