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題名:運用資料探勘技術於電腦化適性測驗之研究
作者:張裕昌
作者(外文):CHANG, YU-CHANG
校院名稱:國立臺灣科技大學
系所名稱:管理研究所
指導教授:吳宗成
學位類別:博士
出版日期:2015
主題關鍵詞:電腦化適性測驗KNN 方法不完整作答題目not reachedK-Nearest Neighbor solutionComputerized Adaptive Test
原始連結:連回原系統網址new window
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缺失資料對於一般的調查研究或實驗數據都有相當的影響。而在電腦適應化測試中(CAT),不完整作答題目(not-reached items)就是一種缺失資料,此會導致嚴重的能力估計錯誤。以往的研究皆試圖從評分規則的角度來解決這個問題。本研究利用資料探勘中的KNN方法來推算能力估計值。結果發現KNN方法優於以往的方式,KNN方法的性能顯然比以前提出的方法更好。結論發現,運用資料探勘技術於電腦化適性測驗,有相當好的效果,其效能也較佳。
Missing data is an inherent feature of most surveys or assessments that involve human subjects. In a Computerized Adaptive Test (CAT), not reached item is a kind of missing data issue which causes serious ability estimation problem. Previous studies tried to resolve this issue from the perspective of scoring rule. This study utilized a K-Nearest Neighbor (KNN) solution based on data mining method to imputethe ability estimation for not-reached items. The results indicated that the predominant of KNN methodwas not obvious when the value of k was less than 10. While the number of neighbor was larger than 20, the performance of KNN method was apparently better thanprevious proposed methods. Overall, the results indicated that the data mining mechanism might provide a better solution for not reached item problem.
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