An Online Learning Behavior Analysis Based on Bayesian Network Model

Feng Guang; Pan Ting-feng; Wu Wen-yan

doi:10.12052/gdutxb.210067

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An Online Learning Behavior Analysis Based on Bayesian Network Model

更新时间：2026-05-27

- An Online Learning Behavior Analysis Based on Bayesian Network Model
- Vol. 39, Issue 3, Pages: 41-48(2022)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210067
  CLC： G434;TP399
- Online First：10 May 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
冯广, 潘庭锋, 伍文燕. 基于贝叶斯网络模型的在线学习行为分析[J]. 广东工业大学学报, 2022, 39(3): 41-48.

Feng Guang, Pan Ting-feng, Wu Wen-yan. An Online Learning Behavior Analysis Based on Bayesian Network Model[J]. 2022, 39(3): 41-48.
冯广, 潘庭锋, 伍文燕. 基于贝叶斯网络模型的在线学习行为分析[J]. 广东工业大学学报, 2022, 39(3): 41-48. DOI： 10.12052/gdutxb.210067.

Feng Guang, Pan Ting-feng, Wu Wen-yan. An Online Learning Behavior Analysis Based on Bayesian Network Model[J]. 2022, 39(3): 41-48. DOI： 10.12052/gdutxb.210067.

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。

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.

关键词

Keywords

references

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