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arXiv · 2008.05849

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

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

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Ahmed Alamri, Mohammad Alshehri, Alexandra I. Cristea, Filipe D. Pereira, Elaine Oliveira, Lei Shi, Craig Stewart. 2020-08-12. Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities. https://doi.org/10.1007/978-3-030-22244-4_20

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