1 / 15

Strategic Data-Informed Retention Interventions in Distance Learning

Learn how predictive modeling and proactive student support are used to improve retention rates for at-risk students in distance learning. This overview of a strategic approach offers insights and outcomes from a project at The Open University. Explore the phases, interventions, and future steps in enhancing student success.

plerch
Download Presentation

Strategic Data-Informed Retention Interventions in Distance Learning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The issue of retention in distance learning: a strategic approach to using data to inform retention interventionsAlison Gilmour and Avinash BoroowaThe Open University

  2. Struggling students want to be noticed.

  3. Overview: our retention project Proactive student support Predictive modelling Studying more than 120 credits Objective To improve the retention rate of ‘at risk’ students as defined by the Strategy and Information Office Predictive Model

  4. Overview Multiple project drivers

  5. Project Team Various staff from The Open University in Scotland including ALs, Educational Advisors and Learning Development Strategy & Information Office Learning & Teaching Innovation

  6. Phase 1: SIOPM What are the student probabilities? Probabilities are generated for: - retention to the above milestones - module completion - module pass - return in the following academic year

  7. Phase 1: The SIOPM How are the student probabilities generated? Different resilience factors influence the model at different points in the module.

  8. 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 SIOPM in predicting non-completions for students in Scotland for 2014J? • IOPM October 2014 completion predictions were compared with actual completion status at the end of the module • How does the using the SIOPM to predict non-completion, compare with using a single variable?

  9. Identifying ‘at risk’ students Step 1: Comparing Selection Methods

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

  11. Identification of ‘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.

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

  13. Interim Data

  14. Next Steps and Phase 3 Some caution … but encouraging signs in the interim data • Data on completion (July 2017) • Disaggregation of students within Intervention Group (a difference between those who were contacted by SMS/ Telephone compared to SMS/ Email) • Consideration of students who moved out of the band • Currently exploring the potential of broader 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 • Planning for Phase 3 – Changing the intervention? Running additional interventions with the same band? Running the same intervention with another band?

  15. Contact Alison Gilmour, The Open University in Scotland alison.gilmour@open.ac.uk Broader Project Team includes: AvinashBoroowa, Lucy Macleod, Hannah Jones, Galina Naydenova, Rebecca Ward and ChristotheaHerodotou.

More Related