  The effectiveness of learning in massive open online courses (MOOCs) can be
significantly enhanced by introducing personalized intervention schemes which
rely on building predictive models of student learning behaviors such as some
engagement or performance indicators. A major challenge that has to be
addressed when building such models is to design handcrafted features that are
effective for the prediction task at hand. In this paper, we make the first
attempt to solve the feature learning problem by taking the unsupervised
learning approach to learn a compact representation of the raw features with a
large degree of redundancy. Specifically, in order to capture the underlying
learning patterns in the content domain and the temporal nature of the
clickstream data, we train a modified auto-encoder (AE) combined with the long
short-term memory (LSTM) network to obtain a fixed-length embedding for each
input sequence. When compared with the original features, the new features that
correspond to the embedding obtained by the modified LSTM-AE are not only more
parsimonious but also more discriminative for our prediction task. Using simple
supervised learning models, the learned features can improve the prediction
accuracy by up to 17% compared with the supervised neural networks and reduce
overfitting to the dominant low-performing group of students, specifically in
the task of predicting students' performance. Our approach is generic in the
sense that it is not restricted to a specific supervised learning model nor a
specific prediction task for MOOC learning analytics.
