Abstract: Learning style can significantly affect the students learning in the network environment. Bayesian network is an important means to realize the automatic detection of network learning style, and TAN Bayesian network which improves the naive bias network has better classification accuracy. Based on the FSLSM model, this paper puts forward a learning style model based on TAN Bayesian network learning according to the presupposed learning style, which can realize the automatic detection of students learning style by mining these data come from students’ network study behaviors. The experimental results show that the TAN learning style model has higher accuracy of detection compared with BN algorithm.

Key words: learning style, TAN Bayesian network, automatic detection

摘要： 学习风格能明显地影响学生在网络环境下的学习效果。贝叶斯网络是实现学习风格自动检测的重要手段，而TAN贝叶斯网络作为改进的朴素贝叶斯网络，具有更优的分类精度。以FSLSM模型为基础，提出了基于学习风格预设的TAN贝叶斯网络学习风格模型，通过挖掘学生的网络学习行为实现学习风格的自动检测。通过实验将BN算法和TAN算法进行了比较，实验结果表明TAN学习风格模型检测具有更高的准确性。

关键词: 学习风格, TAN贝叶斯网络, 自动检测