1. The Many Faces of Learning Analytics: Adaptive Learning and Teaching at CSU
Simon Welsh
Manager, Adaptive Learning and Teaching Services
Division of Student Learning
Charles Sturt University E:

2. Contents What is Adaptive Learning and Teaching?
Data-Informed Practice
Personalised Support
Personalised Learning

3. 1. What is Adaptive Learning and Teaching?
Adaptive Learning and Teaching is comprised of three elements:
Data-informed practice – using learning and teaching data/analytics to inform decision-making, reflective practice in teaching and design, and management of quality standards
Personalised support – using analytics to support students and their learning in targeted ways through evidence-based interventions and feedback
Personalised learning – using analytics-based technologies to personalise or differentiate learning experiences

4. 1. What is Adaptive Learning and Teaching?
Technology
Capability
Culture

5. 2. Data-Informed Practice
Three elements:
Summative learning and teaching data (academic analytics) – enrolments, progress, attrition:
CSU BI Strategy and Data Warehouse
Course Health Dashboard, School/Faculty Reviews
Evaluative feedback – formative and summative student evaluations, peer reviews, assessment & moderation processes:
Challenge: how to connect these pieces and the role of Learning Analytics in evaluation (if any?)

6. 2. Data-Informed Practice
Three elements:
In process learning and teaching data – variety of measures aligned to quality standards that enable management of emerging issues:
Analytics include assignment returns, QA and publication of Subject Outlines, teacher-student communication in online learning environment
Challenge: data and reporting systems
Challenge: indicators vs direct measures

7. 3. Personalised Support
Who needs support? Four definitions of “At Risk”
Locus of Risk
Focus of Analytic Model
Subject-level
Course-level
Engagement (quality of learning and achievement)
At Risk of Disengagement
At Risk of Exclusion
Attrition
At Risk of Subject Withdrawal
At Risk of Attrition

8. 3. Personalised Support
Which analytics? Depends which way you slice it
Challenge: consistent processes and communication
Locus of Risk
Focus of Analytic Model
Subject-level
Course-level
Engagement
Blackboard Analytics – integrated reports and Retention Centre
Formative and summative assessments
Learning activities (e.g. forum posts)
Blackboard Analytics – bespoke reports and dashboards
Attrition
Early formative assessment tasks
Priority Contact List

9. 4. Personalised Learning
Typical institutional approaches to Learning Analytics would not deliver the kind of personalised learning we sought
Engagement defined in behavioural terms: the Boxer Response vs Adaptation During 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

10. Thanks Simon Welsh Manager, Adaptive Learning and Teaching Services
Division of Student Learning
Charles Sturt University E: