Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses

Cristea, A.; Alamri, Ahmed; Kayama, Mizue; Stewart, Craig; Alsheri, Mohammed; Shi, Lei

Abstract

Whilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. In this study, we are particularly interested in finding a novel early detection mechanism of potential dropout, and thus be able to intervene at an as early time as possible. Additionally, unlike previous studies, we explore a light-weight approach, based on as little data as possible – since different MOOCs store different data on their users – and thus strive to create a truly generalisable method. Therefore, we focus here specifically on the generally available registration date and its relation to the course start date, via a comprehensive, larger than average, longitudinal study of several runs of all MOOC courses at the University of Warwick between 2014-1017, on the less explored European FutureLearn platform. We identify specific periods where different interventions are necessary, and propose, based on statistically significant results, specific pseudo-rules for adaptive feedback.

Citation

Cristea, A., Alamri, A., Kayama, M., Stewart, C., Alsheri, M., & Shi, L. (2018, October). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. Presented at 27th International Conference on Information Systems Development (ISD2018)., Lund, Sweden

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 27th International Conference on Information Systems Development (ISD2018).
• Acceptance Date: Jun 11, 2018
• Online Publication Date: Oct 31, 2018
• Publication Date: Oct 31, 2018
• Deposit Date: Jul 18, 2018
• Publicly Available Date: Aug 2, 2018
• Publisher: Association for Information Systems
• Book Title: Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund University.
• Public URL: https://durham-repository.worktribe.com/output/1144954
• Publisher URL: https://aisel.aisnet.org/isd2014/proceedings2018/Education/5/