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The effectiveness of learning in massive open online courses (MOOCs) can besignificantly enhanced by introducing personalized intervention schemes whichrely on building predictive models of student learning behaviors such as someengagement or performance indicators. A major challenge that has to beaddressed when building such models is to design handcrafted features that areeffective for the prediction task at hand. In this paper, we make the firstattempt to solve the feature learning problem by taking the unsupervisedlearning approach to learn a compact representation of the raw features with alarge degree of redundancy. Specifically, in order to capture the underlyinglearning patterns in the content domain and the temporal nature of theclickstream data, we train a modified auto-encoder (AE) combined with the longshort-term memory (LSTM) network to obtain a fixed-length embedding for eachinput sequence. When compared with the original features, the new features thatcorrespond to the embedding obtained by the modified LSTM-AE are not only moreparsimonious but also more discriminative for our prediction task. Using simplesupervised learning models, the learned features can improve the predictionaccuracy by up to 17% compared with the supervised neural networks and reduceoverfitting to the dominant low-performing group of students, specifically inthe task of predicting students' performance. Our approach is generic in thesense that it is not restricted to a specific supervised learning model nor aspecific prediction task for MOOC learning analytics.

Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses