Adaptive learning system based on Bayesian network using fusion strategy for combining multiple student models -using compound shape for an example

[[abstract]]The main purpose of the research is to explore the educational assessment on the basis of Evidence-Centered Design（ECD） to build a convenient and effective diagnosis system. We use multiple Bayesian networks for modeling assessment data and identifying bugs and sub-skills in The “Compound Shape” of Mathematics in Grade 6. This research integrates the opinion of the experts, scholars and primary school teachers. Also, the multimedia computer is devised for Diagnostic Testing and computerizes adaptive remedial instruction with the system. Students can receive not only individual diagnostic tests. But adequate and in-time computerized adaptive remedial instruction. Evaluation Diagnosis and remedy can be achieved simultaneously. The findings of this research are as follows: 1. Multiple Bayesian networks did enhance the recognition level. 2. The system could diagnose students’ errors, which shows that the adaptive test based on the multiple Bayesian networks was effective. 3. The computerized adaptive remedial instruction was testified to be able to replace written tests in a convenient and time-saving way. 4. After adopting computerized adaptive remedial instruction, the bugs of most students were reduced and their skills were improved. It revealed that the computerized adaptive remedial instruction did help enhance students’ learning effects. 5. The distribution of students’ bugs and skills varied with districts where the schools were located and the teachers’ teaching methods