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While Massive Open Online Course (MOOCs) platforms provide knowledge in a newand unique way, the very high number of dropouts is a significant drawback.Several features are considered to contribute towards learner attrition or lackof interest, which may lead to disengagement or total dropout. The jury isstill out on which factors are the most appropriate predictors. However, theliterature agrees that early prediction is vital to allow for a timelyintervention. Whilst feature-rich predictors may have the best chance for highaccuracy, they may be unwieldy. This study aims to predict learner dropoutearly-on, from the first week, by comparing several machine-learningapproaches, including Random Forest, Adaptive Boost, XGBoost and GradientBoostClassifiers. The results show promising accuracies (82%-94%) using as little as2 features. We show that the accuracies obtained outperform state of the artapproaches, even when the latter deploy several features.

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