基于贝叶斯网络模型的在线学习行为分析

doi:10.12052/gdutxb.210067

Abstract

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

CLC Number:

G434

Feng Guang, Pan Ting-feng, Wu Wen-yan. An Online Learning Behavior Analysis Based on Bayesian Network Model[J].Journal of Guangdong University of Technology, 2022, 39(03): 41-48.

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[1]	Ma Fei, Li Juan. A Research on the Classification of Learners and Patterns of Learning Behavior Based on Cluster Algorithms under MOOCs’ Environment [J]. Journal of Guangdong University of Technology, 2018, 35(03): 18-23.
[2]	Fang Yuan, Liu Jun-huai, Xie Jing-zhu, Lu Xiao-qing, Zeng Yan-qian, Xie Han-xiong. Public Participation in Decision-making of PPP Project Based on Bayesian Network [J]. Journal of Guangdong University of Technology, 2018, 35(03): 79-86.