Deep Learning for Educational Data Science

This paper is a comprehensive survey of deep learning applications in educational data science, covering knowledge tracing, automated assessment, affect detection, behavior prediction, and recommendation systems across K-12, higher education, and online learning contexts. It reviews various neural network architectures (CNNs, RNNs, transformers) and their applications to educational tasks such as predicting student actions, detecting student affect and behaviors, automatic grading, and personalized content delivery.

With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of various interrelated research communities -- have turned their attention to leveraging these powerful algorithms within the domain of education. Use cases range from advanced knowledge tracing models that can leverage open-ended student essays or snippets of code to automatic affect and behavior detectors that can iden

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