A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs
International Journal of Web Information Systems
ISSN: 1744-0084
Article publication date: 21 June 2019
Issue publication date: 15 October 2019
Abstract
Purpose
Even if MOOCs (massive open online courses) are becoming a trend in distance learning, they suffer from a very high rate of learners’ dropout, and as a result, on average, only 10 per cent of enrolled learners manage to obtain their certificates of achievement. This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to “retain” each type of learner at risk of dropping out.
Design/methodology/approach
This paper presents a methodology to provide predictions on learners’ behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC.
Findings
The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score.
Originality/value
Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.
Keywords
Acknowledgements
This research was done through Stanford University’s Center for Advanced Research through Online Learning (CAROL); the authors are thankful for all the facilities provided. They also wish to express their full gratitude to Kathy Mirzaei for her responsiveness and collaboration. The authors wish to warmly thank Mitchell Stevens, Director of Digital Research and Planning, as well as all in the CAROL Commission for the trust shown to the authors.
Citation
Mourdi, Y., Sadgal, M., El Kabtane, H. and Berrada Fathi, W. (2019), "A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs", International Journal of Web Information Systems, Vol. 15 No. 5, pp. 489-509. https://doi.org/10.1108/IJWIS-11-2018-0080
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited