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Massive Open Online Courses (MOOCs) have become popular platforms for onlinelearning. While MOOCs enable students to study at their own pace, thisflexibility makes it easy for students to drop out of class. In this paper, ourgoal is to predict if a learner is going to drop out within the next week,given clickstream data for the current week. To this end, we present amulti-layer representation learning solution based on branch and bound (BB)algorithm, which learns from low-level clickstreams in an unsupervised manner,produces interpretable results, and avoids manual feature engineering. Inexperiments on Coursera data, we show that our model learns a representationthat allows a simple model to perform similarly well to more complex,task-specific models, and how the BB algorithm enables interpretable results.In our analysis of the observed limitations, we discuss promising futuredirections.

Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning