本研究的主要目的是探討以國小數學「圖形面積」單元為例,包括長方形、正方形、平行四邊形、三角形、梯形5 種圖形,以貝氏網路為推論工具,並結合專家知識結構,建立一套電腦適性診斷測驗題型與補救教學模組。當受試者評量過後,能立即診斷出學生所具備的單元技能、所容易犯的錯誤類型,對於不了解之處,給予即時、適性的電腦動畫補救教學。希望能同時達到評量、診斷、補救教學的功能。 研究結果發現: 1.利用貝氏網路為推論工具,能精準有效的診斷出學童們的錯誤類型及子技能。 2.結合專家知識結構、學生知識結構後,更能精準的判斷出學童的能力指標、子技能及錯誤類型。 3.透過電腦適性化補救教學之後,學生在圖形面積方面,成績明顯進步,並且達到顯著差異,證實本研究具有可行性。 4.電腦適性診斷測驗與完整作答之貝式網路推論相似度高。
The major purpose of this research is to establish a computerized adaptive diagnosis test items and remedial instruction module by investigating elementary mathematics “graphic area,” which includes rectangle, square, parallelogram, triangle, and trapezoid as an example. The research applies Bayesian networks as modeling assessment data combining knowledge structures of experts to establish the module. When the students were tested, the module can identify the skills students have acquired as well as their common error types. It also offers immediate feedback and adaptive remedial teaching by computerized animation, in the hope to provide functions of assessment, diagnosis, and remedial education simultaneously. The results show: 1. The Bayesian networks evaluation mode has applied effectively to the diagnosis of students’ common errors and sub-skills. 2. By integrating the Bayesian networks, knowledge structure of experts, and knowledge structure of students, students' skill indicators, sub-skills, and common error types can be identified more accurately. 3. The progress of students is significant after taking the Computerize adaptive remedial instruction. 4. The similarity between computerized adaptive diagnosis and Bayesian networks stands for high degree.