  This paper addresses a key challenge in MOOC dropout prediction, namely to
build meaningful representations from clickstream data. While a variety of
feature 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 learn
representation for videos and the correlation between videos and clicks. The
results indicate that modeling videos and their correlation with clicks bring
statistically significant improvements in predicting dropout.
