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This paper addresses a key challenge in MOOC dropout prediction, namely tobuild meaningful representations from clickstream data. While a variety offeature extraction techniques have been explored extensively for such purposes,to our knowledge, no prior works have explored modeling of educational content(e.g. video) and their correlation with the learner's behavior (e.g.clickstream) in this context. We bridge this gap by devising a method to learnrepresentation for videos and the correlation between videos and clicks. Theresults indicate that modeling videos and their correlation with clicks bringstatistically significant improvements in predicting dropout.

Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos