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Durham University

Computer Science


Publication details for Mr Ahmed Alamri

Cristea, A., Alamri, Ahmed, Kayama, Mizue, Stewart, Craig, Alsheri, Mohammed & Shi, Lei (2018), Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses, in Andersson, B., Johansson, B., Carlsson, S., Barry, C., Lang, M., Linger, H. & Schneider, C. eds, 27th International Conference on Information Systems Development (ISD2018). Lund, Sweden, Association for Information Systems, 5.

Author(s) from Durham


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.