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

Struggling students want to be noticed.

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

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

The Student Probabilities Model What are the student probabilities?

The Student Probabilities Model How are the student probabilities generated?

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?

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

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

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.

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

Phase 2: Intervention Outcomes

Phase 2: Intervention Outcomes (Module count)

*Groups adjusted to size

Next steps Disaggregation of students within Intervention Group (exploring difference between those who were contacted by SMS/ Telephone compared to SMS/ Email) 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?

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

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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