Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities
This addresses the issue of timely intervention for MOOC dropout, which is critical for educators and platforms, though it is incremental as it builds on existing prediction methods with a focus on feature efficiency.
The study tackled the problem of high dropout rates in MOOCs by predicting learner dropout early using only two features from the first week's activities, achieving accuracies of 82%-94% and outperforming state-of-the-art approaches that use more features.
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.