Frontiers of Digital Education >

2024 , Vol. 1 >Issue 1: 26 - 50

DOI: https://doi.org/10.3868/s110-009-024-0004-9

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

  • Zhang Xiong 1 ,
  • Haoxuan Li 2 ,
  • Zhuang Liu 3 ,
  • Zhuofan Chen 4 ,
  • Hao Zhou 5 ,
  • Wenge Rong 6 ,
  • Yuanxin Ouyang 7
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  • 1. School of Computer Science and Engineering, Beihang University, Beijing 100191, China; School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China
  • 2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • 3. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • 4. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • 5. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • 6. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • 7. School of Computer Science and Engineering, Beihang University, Beijing 100191, China

Published date: 26 Jan 2024

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2024 Higher Education Press
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Abstract

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.

Key words： personalized education, data mining, educational recommendation (ER), cognitive diagnosis, knowledge tracing, learning analysis

Cite this article

Zhang Xiong , Haoxuan Li , Zhuang Liu , Zhuofan Chen , Hao Zhou , Wenge Rong , Yuanxin Ouyang . A Review of Data Mining in Personalized Education: Current Trends and Future Prospects[J]. Frontiers of Digital Education, 2024 , 1(1) : 26 -50 . DOI: 10.3868/s110-009-024-0004-9

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