A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

TL;DR

This survey reviews how deep learning techniques are applied in educational data mining, covering scenarios like knowledge tracing and student behavior detection, and discusses datasets, challenges, and future trends.

Contribution

It provides a comprehensive overview of deep learning applications in EDM, highlighting recent advancements, challenges, and future research directions.

Findings

Deep learning enhances accuracy in student performance prediction.

Various datasets and tools support EDM research.

Emerging trends include personalized learning and adaptive systems.

Abstract

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, student behavior detection, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. We then analyze the practical challenges in EDM and propose targeted solutions. Finally, we point out emerging trends and future directions in this research area.