| Summary: | 碩士 === 元智大學 === 生物與醫學資訊碩士學位學程 === 100 === Self-explaining is a self-constructive learning activity, which engages students in clarifying and explaining the content and self-monitoring their understanding of the content. Students may generate many kinds of self-explanations, such as re-reading, paraphrase, bridging inference, prior knowledge inference, logic inference, self-monitoring. In general, student explanations need to be classified by human experts, and the classification is time-consuming and labor-intensive. This study applies data mining techniques to automatically classify student explanations. This study adopts vector space model to represent student explanations and applies K-nearest neighbor mechanism to classify student explanations. This study investigates and compares five K-nearest neighbor classification mechanisms: traditional K-Nearest Neighbor, adaptive k-Nearest Neighbor, adaptive k-Nearest Neighbor which includes content for self-explaining, adaptive k-Nearest Neighbor which includes content for self-explaining and excludes common words, adaptive k-Nearest Neighbor with multiple classification. The evaluation results show that adaptive k-Nearest Neighbor with multiple classification has best classification correctness. The correctness of classifying self-monitoring is about 90% and total correctness of classification is about 70%.
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