广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (03): 41-48.doi: 10.12052/gdutxb.210067

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基于贝叶斯网络模型的在线学习行为分析

冯广1, 潘庭锋2, 伍文燕3   

  1. 1. 广东工业大学 自动化学院, 广东 广州 510006;
    2. 广东工业大学 计算机学院, 广东 广州 510006;
    3. 广东工业大学 网络信息与现代教育技术中心, 广东 广州 510006
  • 收稿日期:2021-04-25 出版日期:2022-05-10 发布日期:2022-05-19
  • 通信作者: 伍文燕(1989-),女,高级工程师,硕士,主要研究方向为教育信息化、人工智能、大数据,E-mail:wuwy@gdut.edu.cn
  • 作者简介:冯广(1973-)，男，教授级高级实验师，博士，主要研究方向为网络控制、机器学习、大数据
  • 基金资助:
    国家自然科学基金资助项目(71671048)

An Online Learning Behavior Analysis Based on Bayesian Network Model

Feng Guang1, Pan Ting-feng2, Wu Wen-yan3   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    3. Center of Campus Network & Modern Educational Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-04-25 Online:2022-05-10 Published:2022-05-19

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摘要： 线上线下结合的教学模式是未来教学的一个趋势，每一个学生的学习行为会直接影响学习结果，因此研究学习者学习行为对学习成绩的影响程度是目前的研究重点。目前常见的评价模型存在可信程度较低、可解释性较弱等问题，本文使用基于证据推理的贝叶斯网络(Bayes Network, BN)能够有效地解决这一问题。把方法应用在学习行为分析上，与常用的机器模型和深度学习模型进行比较，表现出更低的误差和更强的可解释性。

关键词: 在线教育, 贝叶斯网络, 学习行为, 联合树, 可解释性

Abstract: The combination of online and offline teaching mode is a trend of teaching in the future. The previous researches show that learners’ learning behavior can directly affect the learning results, so the research focuses on studying the influence level of learning behavior on academic performance. For the current common evaluation models, there are still some problems such as low credibility and weak interpretability. In this study, the Bayesian network (BN) based on evidential reasoning can effectively solve this problem. Compared with the commonly used machine model and deep learning model, it shows a lower error rate and a stronger interpretability.

Key words: online education, Bayesian network, learning behavior, junction tree, interpretability

中图分类号: 

  • G434
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[1] 马飞, 李娟. 基于聚类算法的MOOCs学习者分类及学习行为模式研究[J]. 广东工业大学学报, 2018, 35(03): 18-23.
[2] 方媛, 刘俊槐, 谢晶珠, 卢晓晴, 曾妍倩, 谢汉雄. 基于贝叶斯网络的公众参与PPP项目决策研究[J]. 广东工业大学学报, 2018, 35(03): 79-86.
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