Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling

Chen, Clarence, Pardos, Zachary

arXiv.org Machine Learning 

This paper presents several strategies that can improve neu - ral network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previ - ously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM network s enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.

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