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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 1-14.doi: 10.6040/j.issn.1672-3961.0.2021.489

• 机器学习与数据挖掘 •    下一篇

任务粒度视角下的学生成绩预测研究综述

聂秀山1(),马玉玲1,*(),乔慧妍1,郭杰1,崔超然2,于志云,刘兴波1,尹义龙3   

  1. 1. 山东建筑大学计算机科学与技术学院, 山东 济南 250101
    2. 山东财经大学计算机科学与技术学院, 山东 济南 250014
    3. 山东大学软件学院, 山东 济南 250101
  • 收稿日期:2021-10-11 出版日期:2022-04-20 发布日期:2022-04-20
  • 通讯作者: 马玉玲 E-mail:niexsh@hotmail.com;mayuling20@sdjzu.edu.cn
  • 作者简介:聂秀山(1981—)，男，江苏徐州人，教授，博士，主要研究方向为机器学习与数据挖掘。E-mail：niexsh@hotmail.com|聂秀山, 1981年生, 江苏徐州人, 博士, 教授, 山东省泰山学者青年专家, 山东省杰出青年基金获得者, 主要从事机器学习与数据挖掘、教育大数据分析、智能媒体计算方面的研究工作。中国计算机学会(CCF)高级会员, 中国人工智能学会青年工委的常务委员, 山东省人工智能学会常务理事、学术工委秘书长。在TKDE、TIP、IJCAI、软件学报等国内外重要期刊和会议发表论文50余篇
  • 基金资助:
    国家自然科学基金资助项目(62177031);国家自然科学基金资助项目(62077033);山东省自然科学基金资助项目(ZR2021MF044);山东省教育教学研究课题资助项目(2021JXY012);教育部产学合作协同育人项目(202102423045);山东省教育科学“十三五”规划人工智能教育专项一般资助课题(BYZN201905)

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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摘要：

学生成绩预测作为教育数据挖掘领域重要的研究分支之一, 学者们已开展了大批卓有成效的研究工作, 但对现有文献进行调查、梳理的综述性研究仍相对缺乏。立足于不同的应用场景, 以学生成绩预测研究的任务粒度为视角, 从答题表现预测、课程成绩预测、综合学习表现预测等3个方面, 详细介绍学生成绩预测研究所采用的技术和方法, 并介绍目前学生成绩预测研究在真实教学场景中的应用情况, 从而为科研和教育管理工作者提供更有针对性的参考信息。

关键词: 教育数据挖掘, 学生学业表现预测, 机器学习, 智慧教育, 个性化教学

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

中图分类号: 

  • TP391

图1

贝叶斯知识追踪模型框架"

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