基于贝叶斯网络模型的在线学习行为分析

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。

An Online Learning Behavior Analysis Based on Bayesian Network Model

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.