本研究以國小六年級數學領域「柱體與錐體」單元為研究範園,採用其有良好的預測及診斷能力的貝氏網路做為診斷錯誤概念的工具,並建置一套適用於「柱體與錐體」教學可用的電腦化適性診斷測驗及補救教學系統,來提升學生學習成效。根據本研究實驗的結果,得到以下結論: 1.在最佳單一貝氏網路結構難尋的情況下,就現有的貝氏網路結構來進行融合以獲取較佳的辨識效果,是可行的方法。 2. 適性診斷測驗可與貝氏網路推論模式結合,達到節省施測時間的目標。 3. 以貝氏網路為基礎的適性診斷測驗,應用於診斷國小六年級「柱體與錐體」單元的子技能與錯誤類型之有無,具有良好的診斷效果。 4. 本研究建置的電腦化適性診斷測驗及補救教學系統,能節省施測時間與提升學習成效。
The main purpose of the research is to build an adaptive diagnostic test and remedial instruction system. We use Bayesian networks to make models of mathematical concepts and identify bugs and sub-skills in "prism, pyramid, cylinder and cone" unit. Besides、we try to combine multiple Bayesian networks to get better classification results than single Bayesian networks. The results show that the fusion method "sub-structure fusion", with dynamic cut-point selection can improve the classification accuracy. At last, we experiment on sixth grade elementary school student with the system. The results show that using Bayesian networks to diagnose the existence of bugs and sub-skills in individual students can get good performance. Also, students have made great progress in their mathematics studies.