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arXiv 2001.083332020-01cs.LGcs.CYstat.ML

Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling

University of California, Berkeley（加州大学伯克利分校）；UC Berkeley School of Information（加州大学伯克利分校信息学院）

Clarence Chen, Zachary Pardos

备注：: 4 pages, 0 figures, accepted to EDM 2020

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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