本研究旨在建立一套以國小六年級數學領域分數四則單元為評量內容的電腦個別化測驗系統,探討知識結構結合貝氏網路作為推論工具的評量診斷模式是否方便有效。另外以電腦多媒體元件製作數位學習內容作為補救教學,希望藉由本系統讓學生可以接受個別化的診斷測驗,並施予適性化、即時化的電腦補救教學,期能同時達到評量、診斷、補救教學的功能。 研究結果發現: 1. 以知識結構結合貝氏網路作為推論工具有提昇辨識率的效果,且以結合專家與學生知識結構的貝氏網路表現最佳。在分類決斷值(threshold)選取方面,採用動態分類決斷值選取法的辨識率優於固定分類決斷值選取法,可以達到最高的平均辨識率。 2. 本研究的測驗系統,可有效應用於診斷學生之錯誤類型與子技能,能快速有效代替傳統人工判斷學生在子技能及錯誤類型之有無。電腦適性測驗施測的平均施測題數是13.7 題,與紙筆測驗相比,平均可以節省6.3 題。 3. 透過電腦適性化補救教學後,學生在「分數四則」概念的測驗表現上具有顯著差異,平均分數有進步學習成效明顯。另外,本系統能將同分數但不同錯誤概念的學生,經診斷後進行不同的適性補救教學活動,是傳統測驗與補救教學中較不足的,也是本系統的一大特點與優勢。
This research is in accordance with four factors including indicator, skill, bug and experts knowledge structure. These factors are carried into questions for tests of “Calculation of Fractions” in elementary school mathematics. The test results will be analyzed for understanding the student knowledge structures. And the results are combined by Bayesian Networks to be the basis of questions-choosing for the Computerized Adaptive Diagnostic Test. In addition, the Computerized Adaptive Remedial Instruction is edited by bug and skill factor, and constituted it as a set of adaptive learning system. The system is tested further and evaluated its performance. Some findings are briefly outlined as follows: 1. Computerized Adaptive Diagnostic Test is effective under Bayesian Networks deduction. The classification accuracy can be increased by combining Bayesian Networks and knowledge structures. The classification accuracy of dynamic cut-point selection is better than fixed cut-point selection. 2. The number of items tested by students in the Computerized Adaptive Diagnostic Test System is 13.7 averagely. This system can save 6.3 items averagely, and the test-taking time is also saved simultaneously. 3. The progress of students is significant after the Adaptive Remedial Instruction. Therefore, Computerized Adaptive Diagnostic Test System and Computerized Adaptive Remedial Instruction, proposed in this study can factually test and remedy students’ abilities individually.