Skip navigation

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

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

Herodotou, Christothea, Naydenova, Galina, Boroowa, Avi, Gilmour, Alison ORCID: 0000-0003-2256-1995 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. ISSN 1929-7750 (Online) (doi:https://doi.org/10.18608/jla.2020.72.4)

[img]
Preview
PDF (Publisher's PDF - Open Access)
31014 GILMOUR_Learning_Analytics_and_Motivational_Interventions_(OA)_2020.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (782kB) | Preview

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.

Item Type: Article
Additional Information: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Uncontrolled Keywords: predictive learning analytics, motivational interventions, student support, distance learning, randomized control trial
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Faculty / Department / Research Group: Educational Development Unit
Related URLs:
Last Modified: 07 Feb 2021 00:27
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/31014

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics