This document outlines Charles Sturt University's vision for analytics-driven adaptive learning and teaching. It discusses CSU's establishment of a learning analytics strategy and team in 2013. The vision goes beyond typical institutional approaches by focusing on adaptation during learning through analysis of knowledge representations and students' digital footprints. It also recognizes learning analytics as a learning design challenge. To support this vision, CSU developed the Adaptive Learning and Teaching Services team to build capability among staff and partner with academics on research.

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Dreaming of Electric Sheep
CSU’s Vision for Analytics-Driven Adaptive Learning and
Teaching
Simon Welsh
Manager, Adaptive Learning & Teaching Services
Division of Student Learning
Charles Sturt University
siwelsh@csu.edu.au
http://www.csu.edu.au/division/student-
learning/analytics-and-evaluations
Assoc Prof Philip Uys
Director, Learning Technologies Unit
Division of Student Learning
Charles Sturt University
puys@csu.edu.au
http://www.csu.edu.au/division/student-
learning/home/technologies-for-learning-and-teaching

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Contents
1. Learning Analytics at CSU
2. The Adaptive Learning and Teaching Vision
3. The Organisational Arrangements

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1. Learning Analytics at CSU
• 2013:
• Established a cross-institutional Learning Analytics Working Party
• Developed a Learning Analytics Strategy
• Appointed a Senior Learning Analytics Officer to lead implementation
of Strategy
• 2014:
• Implemented “At Risk” analytics
capability
• Developed Model of Learning
Analytics to guide implementation
• Mapping of drivers of student
success

4. DIVISION OF STUDENT LEARNING
1. Learning Analytics at CSU
• 2015:
• Implemented Blackboard Analytics
• Established analytics Community of Practice
• Developed Code of Practice and Policy Framework
• Strategic development of the CSU data warehouse
• New Distance Education Strategy and Online Learning Model

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2. The Adaptive Learning and Teaching Vision
Typical institutional approaches to Learning Analytics would not
deliver the kind of personalised learning we sought:
• Engagement defined in behavioural terms, more about
management than learning
• Over-reliance on inductive techniques, using simplistic metrics,
when dealing with a complex process (learning)
• “Lag” Feedback & the Boxer Response vs Adaptation During
Learning
• “Incompatibility” of certain learning designs
• Ethical issues
• Raising the floor vs raising the ceiling

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2. The Adaptive Learning and Teaching Vision
So, what’s our response?
• Focus on the change not the input: Adaptive Learning and
Teaching, as a pathway to personalised online learning
• Recognise Learning Analytics as a learning design challenge to
create representations of knowledge and embed analytics in
formative feedback processes
• Adaptation (of learning activities and feedback) during learning
through analysis of knowledge representations and a students’
digital footprint
• Need to go beyond current adaptive learning approaches and
support a wider range of pedagogies

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3. The Organisational Arrangements
• Q: “Who does your Learning Analytics?” A: We all do
• Developed the Adaptive Learning & Teaching Services team:
• Focus on supporting personalised support, personalised learning and data-
informed practice by building capability of people, as well as systems
• Partner with academics on research and evaluation

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Thanks
&
Questions
Simon Welsh
Manager, Adaptive Learning & Teaching Services
Division of Student Learning
Charles Sturt University
siwelsh@csu.edu.au
http://www.csu.edu.au/division/student-
learning/analytics-and-evaluations
Assoc Prof Philip Uys
Director, Learning Technologies Unit
Division of Student Learning
Charles Sturt University
puys@csu.edu.au
http://www.csu.edu.au/division/student-
learning/home/technologies-for-learning-and-teaching