單一貝氏網路(Bayesian network) 在教育測驗診斷上通常都有辨識率提升的瓶頸,據研究顯示要提高辨識率可以透過結合多重貝氏網路來達成,其中以SVM(Support Vector Machine)結合多重貝氏網路(楊智為,2007)是目前提升辨識率效果最好的,因此本研究將利用SVM結合多重貝氏網路做為推論工具,運用電腦的快速演算,建置一套電腦適性化的評量系統。 研究結果: ㄧ、多重貝氏網路的結合,可以提高辨識率,其中以SVM結合多重貝氏網路的辨識效果最好。 二、在電腦適性化診斷測驗中省題率達25%以上。 三、在電腦化補救教學後,受試者的成績明顯提高。 本研究以SVM結合多重貝氏網路的電腦適性化診斷測驗及補救教學的適性學習系統,確實可達到「因材施測」、「因材施教」的目的,且具有良好的成效。 關鍵字:多重貝氏網路、SVM、電腦適性化診斷測驗、電腦化補救教學
Single Bayesian network usually has bottlenecks improving of distinguishing rate in educational testing. According to studies to improve the distinguishing rate can reach through fusing multiple Bayesian network,especially based on SVM (Support Vector Machine) fused multiple Bayesian network (Yang Zhi Way, 2006) It is the best result to improve the distinguishing rate at present, thus, this research will utilize the inference tool based on SVM fused multiple Bayesian network, using mathematical calculations fast of the computer, establish a computerized adaptiv learning system. Results: 1. To improve the distinguishing rate can reach through fused multiple Bayesian network especially based on SVM which fused multiple Bayesian network is the best. 2. The province topic ratio reaches above 25% in computerized adaptive diagnostic test. 3. The result obviously elevates after computerized adaptive remedial Instruction activities. An Adaptive Learning System based on SVM fused multiple Bayesian Networks can test and teach students in accordance with their aptitude. Key word: Multiple Bayesian Networks , SVM , Computerized Adaptive Diagnostic Test, Computerized Adaptive Remedial Instruction.