Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (2): 1-14.doi: 10.6040/j.issn.1672-3961.0.2021.489

• Machine Learning & Data Mining •     Next Articles

Survey on student academic performance prediction from the perspective of task granularity

Xiushan NIE1(),Yuling MA1,*(),Huiyan QIAO1,Jie GUO1,Chaoran CUI2,Zhiyun YU,Xingbo LIU1,Yilong YIN3   

  1. 1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China
    2. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, Shandong, China
    3. School of Software, Shandong University, Jinan 250101, Shandong, China
  • Received:2021-10-11 Online:2022-04-20 Published:2022-04-20
  • Contact: Yuling MA E-mail:niexsh@hotmail.com;mayuling20@sdjzu.edu.cn

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Abstract:

As one of the important research branches in educational data mining domain, student performance prediction was intensively studied. However, a comprehensive review of student performance prediction was still underexplored from the perspective of real applications. This paper detailed the technologies and methods exploited in student performance prediction research from the perspective of task granularity, and then introduced several application-oriented cases of student performance prediction, so as to provide targeted reference information for scientific researchers and educators.

Key words: educational data mining, student academic performance prediction, machine learning, intelligent education, individualized teaching

CLC Number: 

  • TP391

Fig.1

Framework of Bayesian knowledge tracing model"

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