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The use of large pretrained neural networks to create contextualized wordembeddings has drastically improved performance on several natural languageprocessing (NLP) tasks. These computationally expensive models have begun to beapplied to domain-specific NLP tasks such as re-hospitalization prediction fromclinical notes. This paper demonstrates that using large pretrained modelsproduces excellent results on common learning analytics tasks. Pre-trainingdeep language models using student forum data from a wide array of onlinecourses improves performance beyond the state of the art on three textclassification tasks. We also show that a smaller, distilled version of ourmodel produces the best results on two of the three tasks while limitingcomputational cost. We make both models available to the research community atlarge.

EduBERT: Pretrained Deep Language Models for Learning Analytics