  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.
