This document discusses Tribal's learning analytics R&D project. It notes that higher education institutions now have access to large amounts of student data but more work is needed to define student success and develop predictive models. The project aims to build models that identify students in need of support, compare students to their peers, and provide insights to help staff steer students towards success. Visualizations of predictions will supplement model output with activity data to inform staff discussions.

1. www.triballabs.net
Tribal Learning Analytics R&D Project
Chris Ballard – Innovation Consultant (Analytics)
6th December 2012
@chrisaballard

2. Who are we?
Tribal Learning Analytics R&D Project

3. Our work with Learning Analytics
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4. “Every ….. days we create as much
information as we did from the dawn of
civilization up until 2003. That’s
something like five exabytes of data.”
Eric Schmidt (Google CEO)

5. Do we have Big Data in Higher
Education?
Tribal Learning Analytics R&D Project

6. Do we have Big Data in Higher
Education?
Yes, but…
Big is relative.
Tribal Learning Analytics R&D Project

7. Factors affecting Retention and
Success
Academic Integration Engagement Circumstances
Social Integration Preparation for HE
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8. Factors affecting Retention and
Success
Academic Integration Engagement Circumstances
Grades VLE Activity Social Background
Progress Library Activity Proximity
Student Debt
Social Integration Preparation for HE
Forum interaction Demographics
Social networks Qualifications
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9. Objectives for project
Supporting the student
Predict which students who may require additional support
Comparison to peers
Identify potential problem areas
Give staff better insight
Enable “actionable insights”
Steer students towards success
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10. Student “Success”
Withdrawal
True False
Quantitative
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11. Student “Success”
Withdrawal Success
True False Completed Passed
Reached
average
Exceeded
expectations
Academic
Satisfaction …
Success
Quantitative Qualitative
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12. Quantifying academic success
All students Cluster Individual
Student
Attainment of
Median grade
Median Grade cluster
for cluster
median grade
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13. Student Information System Activity Data Engagement
Academic Academic Integration
performance at Course Enrolment Attendance VLE Usage
entrance
Preparation for HE
Social Integration
Contact with
UCAS Application Fees Library Usage
support services
Future data sources
Social background Assessments Contact with tutors Campus PC Usage
Demographics Proximity Social interaction Door access
Open Data
IMD Spatial
Predictive Model
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14. Visualising Predictions
 Predictions should help staff make informed decisions
 Predictions from a model are just part of the picture
 Predictions should be combined with staff experience and knowledge
 Predictions should empower staff to ask the right questions
Predictions are a tool to help staff understand
where there might be issues and inform
subsequent discussions
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21. Summary
 Student Success
 Often focused on “academic success”
 Are the current definitions of student success too simplistic?
 Predictive Model
 The model needs to be “transparent”
 Allow practitioners to see where likely issues may lie
 Combining diverse models results in greater predictive accuracy
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22. Summary
 Data Visualisation for Learning Analytics
 Should be focused on providing information to help inform discussions
 Supplement predictions with analytics based on underlying activity data
 Comparison with cohort enables comparative judgements to be made
 Actionable Insights
 Embedding intervention recording, management and workflow
 Feedback loop to understand whether interventions make a difference
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24. Chris Ballard
Innovation Consultant, Tribal
twitter: @chrisaballard
blog: www.triballabs.net
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