1. Using predictive analytics to support students: Case study 1
Alison Gilmour, Avinash Boroowa and Christothea Herodotou

2. Struggling students want to be noticed.

3. Overview: our retention project
Proactive student support
Predictive Learning Analytics
Objective
To improve the retention rate of ‘at risk’ students as defined by SPM

4. Cross-institutional Collaboration
Staff from The Open University in Scotland including Associate Lecturer, Educational Adviser, Data Analyst and Learning Development Learning Enhancement Manager and Student Support Manager
Staff from the Strategy & Information Office including Data Analyst
Staff from Learning & Teaching Innovation including Product Development Manager

5. The Student Probabilities Model What are the student probabilities?

6. The Student Probabilities Model How are the student probabilities generated?

7. Phase 1: February – June 2016
Testing the application of predictive modelling with a Scottish Cohort
Our aim is to reduce the number of non-completions
How accurate was the SPM in predicting non-completions for students in Scotland for 2014J?
SPM October 2014 completion predictions were compared with actual completion status at the end of the module
How does using the SPM to predict non-completion, compare with using a single variable?

8. Identifying ‘at risk’ students Step 1: Comparing Selection Methods
Selection method Number in Section % of selection Number of who didn’t complete non completers identified

9. Identifying ‘at risk’ students Step 2: Further Refinement of the Selection

10. Identifying ‘at risk’ students Step 2: Further Refinement of the Selection
The lower the range selected, the higher the percentage of non-completers within the selection.
Student Probabilities allow users to refine the selection to identify a number of students that suits the capacity and resources available, by narrowing the selection range.
The example shows the number of students in each probability band, and this can be used to focus on one or more bands to suit both the target number of students, and the target range.

11. Phase 2: Retention Intervention
Rationale:
To ensure students who were identified as ‘at risk’ were contacted to offer additional support if required.
Phase 2: Retention Intervention

12. Phase 2: Intervention Outcomes

13. Phase 2: Intervention Outcomes (Module count)

14. *Groups adjusted to size

15. Next steps
Disaggregation of students within Intervention Group (exploring difference between those who were contacted by SMS/ Telephone compared to SMS/ )
Consideration of students who moved out of the band
Consideration of contextual evidence that would allow us to better understand student behaviour:
Mining of student records for both Control and Intervention Groups (to consider specific module intervention and the broader intervention landscape)
Analytics such as VLE behaviour
Next pilot: Changing the intervention? Repeat interventions with the same band? Repeat intervention with another band? Timing of intervention?

16. Thank you Alison. Gilmour@open. ac
Thank you Project Team includes: Avinash Boroowa, Christothea Herodotou, Hannah Jones, Lucy Macleod, Galina Naydenova and Rebecca Ward.

17. Phase 1: February – June 2016
Testing the application of predictive modelling with a Scottish Cohort
Our aim is to reduce the number of non-completions
How accurate was the SPM in predicting non-completions for students in Scotland for 2014J?
SPM October 2014 completion predictions were compared with actual completion status at the end of the module
How does using the SPM to predict non-completion, compare with using a single variable?

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