OER Knowledge CloudConference ProceedingsItani, AlyaBrisson, LaurentGarlatti, SergeIn this paper, we propose a machine learning based drop-out prediction process for spotting and preventing drop-out among MOOC learners early upon their interaction with the course. Two main goals are perused in this scope, first, achieving an accurate prediction at a specified instant of the course, established with the help of predictive type classifiers. Second, uncovering the underlying reasons for the predicted drop-out established with the help of explicative type classifiers. The related experimental findings show promising results.20182018/07IEEE00223-224France10.1007/978-3-030-03493-1_25doi.ieeecomputersociety.org/10.1109/ICALT.2018.001182161-377Xyesdecision treesmachine learningpredictive modelsradio frequencytestingtrajectorytuningdoi.ieeecomputersociety.org/10.1109/ICALT.2018.00118Mumbai, France, Europe