Predict and Intervene: Addressing the Dropout Problem in a MOOC-based Program

Abstract

Massive Open Online Courses (MOOCs) are an efficient way of delivering knowledge to thousands of learners. However, even among learners who show a clear intention to complete a MOOC, the dropout rate is substantial. This is particularly relevant in the context of MOOC-based educational programs where a funnel of participation can be observed and high dropout rates at early stages of the program significantly reduce the number of learners successfully completing it. In this paper, we propose an approach to identify learners at risk of dropping out from a course, and we design and test an intervention intended to mitigate that risk. We collect course clickstream data from MOOCs of the MITx MicroMasters® in Supply Chain Management program and apply machine learning algorithms to predict potential dropouts. Our final model is able to predict 80% of actual dropouts. Based on these results, we design an intervention aimed to increase learners' motivation and engagement with a MOOC. The intervention consists on sending tailored encouragement emails to at-risk learners, but despite the high email opening rate, it shows no effect in dropout reduction.