Applied research on intelligent tutoring system based on Dynamic Bayesian Network

Xiaoyang DU, Qing Liu


In order to research the higher intelligent in education system, mainly reflected in the student evaluation and diagnostic capabilities, we propose a dynamic Bayesian network based intelligent tutoring system (DBNITS). Application of the EM algorithm and the PC algorithm to establish a Bayesian network structure, and test the system with students, by comparing the experimental results and analysis of variance showed that: DBNITS can make more accurate evaluation and diagnosis according to students’ characteristics, and play a significant role in improving students’ efficiency in learning and enhancing their comprehensive access to knowledge.

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