Predicting dropouts on the successive offering of a MOOC

Abstract

In recent years, we have experienced the rise of e-learning and the growth of available Massive Online Open Courses (MOOCs). Consequently, an increasing number of universities has dedicated resources to the development and publishing of MOOCs on portals. A common practice for operators of such MOOCs is to re-offer the same course over the years with similar modalities and minor improvements. Such re-runs are still affected, as most of the MOOCs, by a low percentage of enrolled users who manage to successfully complete the courses. Hence, analyzing similar MOOCs can provide valuable insights to better understand the reasons of users for dropping out and potentially can help MOOCs' administrators to better shape the structure of the courses to keep users engaged. To that end, we analyze two successive offerings of the same MOOC, created by Curtin University and published on the edX platform. We extract features for our prediction experiment to detect dropouts, considering two different metrics: (i) the percentage of users active time and (ii) the initial week after users first interaction with the MOOC. We train a Boosted Decision Tree classifier with the extracted features from the original run of the MOOC and predict dropouts on its re-run. Furthermore, we identify a set of features that likely indicates whether users will drop out in the future or not. Our results indicate that users interacting with particular tools at the very beginning of a MOOC are more likely to successfully complete the course.