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A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

Yuanguo Lin, Hong Chen, Wei Xia, Fan Lin, Zongyue Wang, Yong Liu

TL;DR

A detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, student behavior detection, performance prediction, and personalized recommendation, and a comprehensive overview of public datasets and processing tools for EDM are provided.

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.

A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

TL;DR

A detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, student behavior detection, performance prediction, and personalized recommendation, and a comprehensive overview of public datasets and processing tools for EDM are provided.

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.
Paper Structure (45 sections, 13 equations, 6 figures, 3 tables)

This paper contains 45 sections, 13 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Supervised Learning
  4. Unsupervised Learning
  5. Reinforcement Learning
  6. Value Function Approach
  7. Policy Search Method
  8. Actor-Critic Algorithm
  9. Educational Scenarios and Corresponding Algorithms
  10. Knowledge Tracing
  11. Supervised Learning
  12. Unsupervised Learning
  13. Reinforcement Learning
  14. Student Behavior Detection
  15. Supervised Learning
  16. ...and 30 more sections

Figures (6)

  • Figure 1: Distribution of year-wise publications (until May 2024) according to different educational scenarios.
  • Figure 2: The process of Recurrent Neural Network piech2015deep, taking knowledge tracing as an example.
  • Figure 3: A simple knowledge tracing schema. Different exercises contains multiple types of concepts with different colors. Whether the exercises are correct or not will affect knowledge tracing's judgment of the student's mastery of a certain knowledge point or exercise liu2021survey.
  • Figure 4: A simple structure of ATGCN model qiu2023ld for student behavior detection.
  • Figure 5: A simple structure of LBDL model liu2023learning for dropout prediction.
  • ...and 1 more figures