A Review of Using Machine Learning Approaches for Precision Education

Hui Luan, Chin Chung Tsai*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

23 引文 斯高帕斯(Scopus)

摘要

In recent years, in the field of education, there has been a clear progressive trend toward precision education. As a rapidly evolving AI technique, machine learning is viewed as an important means to realize it. In this paper, we systematically review 40 empirical studies regarding machine-learning-based precision education. The results showed that the majority of studies focused on the prediction of learning performance or dropouts, and were carried out in online or blended learning environments among university students majoring in computer science or STEM, whereas the data sources were divergent. The commonly used machine learning algorithms, evaluation methods, and validation approaches are presented. The emerging issues and future directions are discussed accordingly.

原文英語
頁(從 - 到)250-266
頁數17
期刊Educational Technology and Society
24
發行號1
出版狀態已發佈 - 2021

ASJC Scopus subject areas

  • 教育
  • 社會學與政治學
  • 工程 (全部)

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