Earliest predictor of dropout in MOOCs: A longitudinal study of futurelearn courses

Cristea, AI, Alamri, A, Kayama, M, Stewart, C, Alshehri, M and Shi, L ORCID: 0000-0001-7119-3207
(2018) Earliest predictor of dropout in MOOCs: A longitudinal study of futurelearn courses. In: The 27th International Conference on Information Systems Development (ISD2018), 2018-8-22 - 2018-8-24, Lund, Sweden.

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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.

Item Type: Conference or Workshop Item (Unspecified)
Depositing User: Symplectic Admin
Date Deposited: 20 Aug 2018 06:46
Last Modified: 19 Jan 2023 01:28
URI: https://livrepository.liverpool.ac.uk/id/eprint/3025225