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<p>This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.</p>

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Educational data mining: a 10-year review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月19日

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  • Emi Kalita,
  • Solomon Sunday Oyelere,
  • Silvia Gaftandzhieva,
  • Kandala N. V. P. S. Rajesh,
  • Senthil Kumar Jagatheesaperumal,
  • Asmaa Mohamed,
  • Yomna M. Elbarawy,
  • Abeer S. Desuky,
  • Sadiq Hussain,
  • Mehmet Akif Cifci,
  • Paraskevi Theodorou,
  • Slavoljub Hilčenko,
  • Jiten Hazarika,
  • Tazid Ali

摘要

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.