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

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...

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Bibliographic Details
Published inJournal of Learning Analytics Vol. 7; no. 2; pp. 72 - 83
Main Authors Herodotou, Christothea, Naydenova, Galina, Boroowa, Avi, Gilmour, Alison, Rienties, Bart
Format Journal Article
LanguageEnglish
Published Society for Learning Analytics Research 01.01.2020
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Summary: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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ISSN:1929-7750
1929-7750
DOI:10.18608/jla.2020.72.4