Deep Learning for Educational Data Science

TL;DR

The chapter surveys how deep learning can be applied in educational data science, outlining architectures, advantages, and limitations, and organizing DL applications into direct educational tasks (e.g., knowledge tracing, automated assessment, affect detection) and indirect uses (e.g., feature extraction, ASR, computer vision). It highlights five DL advantages—increased predictive accuracy, automatic representation learning, input flexibility, continuous training, and transfer learning—while acknowledging key constraints such as interpretability, data needs, and privacy risks. The authors categorize literature into knowledge tracing, prediction, and assessment, then discuss future directions emphasizing transparency, contributions to learning theory, and real-world deployment beyond the lab. Collectively, the work provides a roadmap for leveraging DL to enhance educational outcomes while addressing trust, ethics, and scalability concerns.

Abstract

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 identify when a student is frustrated or aimlessly trying to solve problems unproductively -- and much more. This chapter provides a brief introduction to deep learning, describes some of its advantages and limitations, presents a survey of its many uses in education, and discusses how it may further come to shape the field of educational data science.