| Summary: | 碩士 === 亞洲大學 === 資訊工程學系碩士班 === 95 === This research aims to investigate the effectiveness of different model of inference, with Bayesian Network Theory, in the sub-skills of item structure analysis, using multiplication of fraction as an example. It also compares the effects of adaptive vs. non-adaptive diagnostic tests and adaptive remedial teaching, using students’ item structure as the strategy for the adaptive diagnostic test and computer multimedia animation as the design for the adaptive remedial teaching. Through this system, it is expected that wrong concepts in the process of students’ learning can be diagnosed more effectively, giving students adaptive and individual remedial teaching, and at the same time, achieving the functions of tests, diagnosis and remedial teaching.
The results of this research include:
1. With the inference of Bayesian Network Theory, the structure obtained from ordering Theory when the threshold value is 0.045 can achieve the highest differentiability. when the threshold value in Ordering Theory is 0.045 can achieve the highest differentiability.
2. Students’ item structure with Bayesian Network Theory, as the mode of inference, obtains the highest effectiveness in diagnosing students’ error types and sub-skills pass rate.
3. After the implementation of remedial teaching, students’ average grades and the average sub-skills pass rate are increased significantly. The percentage of the average occurrences of error types is also decreased.
4. The adaptive test saves the use the test items, and in comparison with the full answers in Bayesian Network Theory, the likelihood is over 90% Besides saving the test items, the adaptive diagnostic is also consistent with both the degree of accuracy in anticipation and inference in non-adaptive.
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