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Student model construction of intelligent teaching system based on Bayesian network

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Abstract

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.

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Funding

This work is supported by Inner Mongolia University for Nationalities scientific research projects (NMDYB15023, NMDYB15096) and College Scientific Research Project of Inner Mongolia Autonomous Region (NJZY19156).

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  1. College of Mechanical Engineering, Inner Mongolia University for the Nationalities, Tongliao, 028000, China

    Lan Wu

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  1. Lan Wu
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Correspondence to Lan Wu.

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Wu, L. Student model construction of intelligent teaching system based on Bayesian network. Pers Ubiquit Comput 24, 419–428 (2020). https://doi.org/10.1007/s00779-019-01311-3

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