The intelligent teaching system is the most important in the field of teaching. It uses artificial intelligence technology to bring a lot of help to learners in terms of knowledge and skill acquisition. In this process, human tutors are not required to participate. The student model is the core of the intelligent teaching system. Using the Bayesian network with strong self-learning ability to construct the intelligent teaching system student model can significantly improve the intelligence level of the intelligent teaching system. Firstly, we discussed the basic concepts of the student model of the intelligent teaching system. Then, from the perspective of students’ ability teaching, combined with the students’ learning status and characteristics, the factors influencing the students’ learning process are analyzed. Finally, an intelligent teaching system student model was built based on Bayesian network. This model can objectively evaluate students’ cognitive ability and can infer students’ next action. In addition, the model is also applicable to the online test system, and the experimental results obtained demonstrate the effectiveness of the modified model.

中文翻译：

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基于贝叶斯网络的智能教学系统学生模型的构建

智能教学系统是教学领域中最重要的。它使用人工智能技术为学习者带来了很多知识和技能方面的帮助。在此过程中，不需要人工指导。学生模型是智能教学系统的核心。利用具有较强自学习能力的贝叶斯网络构建智能教学系统的学生模型，可以显着提高智能教学系统的智能水平。首先，我们讨论了智能教学系统学生模型的基本概念。然后，从学生能力教学的角度，结合学生的学习状况和特点，分析了影响学生学习过程的因素。最后，建立了基于贝叶斯网络的智能教学系统学生模型。该模型可以客观地评估学生的认知能力，并可以推断学生的下一步行动。此外，该模型还适用于在线测试系统，并且获得的实验结果证明了改进模型的有效性。