Deep Learning for Educational Data Science

TL;DR

This paper surveys the application of deep learning techniques in educational data science, highlighting its uses in knowledge tracing, affect detection, and future potential in education research.

Contribution

It provides an overview of deep learning's advantages, limitations, and diverse applications in educational data science, offering insights into future developments.

Findings

Deep learning enhances knowledge tracing and affect detection in education.

It reveals both strengths and limitations of deep learning in educational contexts.

The paper discusses future directions for deep learning in educational data science.

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.