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Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities

Alamri, Ahmed; Alshehri, Mohammad; Cristea, Alexandra I.; Pereira, Filipe D.; Oliveira, Elaine; Shi, Lei; Stewart, Craig

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Authors

Mohammad Alshehri

Filipe D. Pereira

Elaine Oliveira

Lei Shi



Contributors

Andre Coy
Editor

Yugo Hayashi
Editor

Maiga Chang
Editor

Abstract

While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. The jury is still out on which factors are the most appropriate predictors. However, the literature agrees that early prediction is vital to allow for a timely intervention. Whilst feature-rich predictors may have the best chance for high accuracy, they may be unwieldy. This study aims to predict learner dropout early-on, from the first week, by comparing several machine-learning approaches, including Random Forest, Adaptive Boost, XGBoost and GradientBoost Classifiers. The results show promising accuracies (82%–94%) using as little as 2 features. We show that the accuracies obtained outperform state of the art approaches, even when the latter deploy several features.

Citation

Alamri, A., Alshehri, M., Cristea, A. I., Pereira, F. D., Oliveira, E., Shi, L., & Stewart, C. (2019). Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities. In A. Coy, Y. Hayashi, & M. Chang (Eds.), Intelligent tutoring systems. ITS 2019 (163-173). Springer Verlag. https://doi.org/10.1007/978-3-030-22244-4_20

Acceptance Date Mar 12, 2019
Online Publication Date May 30, 2019
Publication Date May 30, 2019
Deposit Date Jun 13, 2019
Publicly Available Date Mar 28, 2024
Publisher Springer Verlag
Pages 163-173
Series Title Lecture notes in computer science
Series Number 11528
Book Title Intelligent tutoring systems. ITS 2019.
DOI https://doi.org/10.1007/978-3-030-22244-4_20

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