| Summary: | 碩士 === 國立嘉義大學 === 資訊工程學系研究所 === 97 === Since different person has different learning method that suits him/her, the adaptive learning is hard to be reached through the existed single feedback assessment mechanism. This thesis integrated a set of expert decision tree algorithm to build up the regularity between learning type and teaching strategy. The regularity is embedded into the computer assisted system to set up new diversified teaching assessment system. Experts’ teaching experiments and KLSI Inventory are used to set up the rules of learning type knowledge database in the system. Based on the learning type knowledge database, the system will judge learners’ strength in each stage of the learning period, and divided the learners into four learning types. Then based on the response from teaching material and assessment result, the learning strategy suitable for different types of learners is analyzed. After revise the knowledge regularity correlation, the system will propose a suggestion of learning strategy for the learners to enhance learning effectiveness.
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