- 學位論文
多重貝式網路為基礎之國小扇形周長和扇形面積單元線上診斷測驗與適性補救教學
On-Line Diagnostic Test and Adaptively Remedial Instruction of the girth and area of a fan-shaped unit in elementary school curriculum, based on combining Multiple Bayesian Networks.
摘要
本研究旨在建立一套以國小六年級數學領域「扇形面積與扇形周長」單元為評量內容的線上診斷測驗系統，同時藉著專家知識結構建立五個貝式網路，並藉著結合不同貝氏網路之電腦適性補救教學模組，能診斷出學生的學習成效外，還可以讓學生進行自我主動學習，以此模式反覆練習達臻嫻熟之境地。本研究首先分析指標內容、錯誤類型，建立能力指標專家知識結構，並依此結構進行命題，實施紙筆診斷測驗，測驗完成後，再依據結合不同貝式網路，建立電腦適性診斷測驗施測流程，以進行測驗及評估電腦化適性補救教學之成效。本研究發現： 1. 結合不同貝式網路之辨識率效果比單一貝式網路提升許多。 2. 電腦適性診斷測驗施測的平均施測題數是14.31 題，與紙筆測驗相比，平均可以節省10.69 題。 3. 經過電腦適性補救教學後，學生的平均分數有進步，達到顯著差異。 所以，本研究所提出之結合不同貝式網路之線上診斷測驗系統和適性補救教學模組，確實可以達到「因材施測」與「因材施教」的學習成效。
並列摘要
This research is aimed to establish On-Line Diagnostic Test, based on the unit of the girth and area of a fan-shaped in the 6th grade math curriculum. We can diagnose the effectiveness of students’ learning and facilitate self-initiated learning through the five Bayesian networks constructed by expert knowledge structure and Adaptively Remedial Instruction based on combining Multiple Bayesian Networks modules. Moreover, students can follow the pattern and master the skills by repeated practice. This study first analyzes the target content and the wrong types, establishes ability index of expert knowledge structure, then propounds and carries out the pen-and-paper diagnostic test. After the test, the procedures of Adaptively Remedial Instruction are founded, based on combining Multiple Bayesian Networks, in order to evaluate the effectiveness of Adaptively Remedial Instruction. This paper identifies the followings: 1. The identification accuracy of combining Multiple Bayesian Networks is much higher than single Bayesian Networks. 2. The average number of proposition of the Adaptively Diagnostic Test Instruction is 14.31, which is 10.69 lower than pen-and-paper test. 3. Through Adaptively Remedial Instruction, students’ average scores improve significantly. In conclusion, the On-Line Diagnostic Test and Adaptively Remedial Instruction of the girth and area of a fan-shaped unit in elementary school curriculum, based on combining Multiple Bayesian Networks, which are proposed in this study, are proved to obtain better classification results.