How Can Predictive Learning Analytics and Motivational Interventions Increase Student Retention and Enhance Administrative Support in Distance Education?

Herodotou, Christothea; Naydenova, Galina; Boroowa, Avinash; Gilmour, Alison and Rienties, Bart (2020). How Can Predictive Learning Analytics and Motivational Interventions Increase Student Retention and Enhance Administrative Support in Distance Education? Journal of Learning Analytics, 7(2) pp. 72–83.

DOI: https://doi.org/10.18608/jla.2020.72.4

Abstract

Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are 1) to identify whether and how PLAs can inform the design of motivational interventions and 2) to capture the impact of those interventions on student retention at the Open University UK. A predictive model — the Student Probabilities Model (SPM) — was used to predict the likelihood of a student remaining in a course at the next milestone and eventually completing it. Undergraduate students (N=630) with a low probability of completing their studies were randomly allocated into the control (n=312) and intervention groups (n=318), and contacted by the university Student Support Teams (SSTs) using a set of motivational interventions such as text, phone, and email. The results of the randomized control trial showed statistically significant better student retention outcomes for the intervention group, with the proposed intervention deemed effective in facilitating course completion. The intervention also improved the administration of student support at scale and low cost.

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