Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses 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 autoencoder (AE) combined with the long shortterm memory (LSTM) network to obtain a fixedlength embedding for each input sequence. When compared with the original features, the new features that correspond to the embedding obtained by the modified LSTMAE 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 lowperforming group of students, specifically in the task of predicting studentsu0027 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.

Learning Resource Recommendation Based on Generalized Matrix Factorization and Long ShortTerm Memory Model Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally canu0027t deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named GLSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long ShortTerm Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.