Student model for personalized instructional system

In light of the present situation research on Student model in Personalized Instructional System at home and abroad. This paper discusses a student model using Bayesian network inference as well as cognitive theories to evaluate student´s cognition ability precisely and objectively. We set up Bayesian network for expressing a structure of special domain knowledge,and then put the result of student´s congnitive ability into Bayesian network ,then use polytree algorithm to update Bayesian network. This approach has been proved to win a more precise characterization since it overcomes the defect, saying independent or discreted, of traditional way which deals with students´ information separately. We model the student behavior data, such as scores, in the framework of Bloom´s Taxonomy and transmit these evidence to Bayesian network, and in that situation we calculate the cognitive map of student´s learning. A course summarization of the algorithm is also discussed. Experimental results show that this approach is effective and practical as in the application.