透過您的圖書館登入
IP:3.141.202.54
  • 學位論文

以貝氏網路為基礎之能力指標測驗編製及補救教學動畫製作–以三年級數學領域之「數與量的認識」相關指標為例

Competence Indicators Test and Remedial Instruction Developments Based on Bayesian Networks – The “Figures and Quantity” Relates Indicators of Mathematics in Grade 3

指導教授 : 劉湘川
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


摘要 本研究結合教授及數學科教師的意見,目的在建立以貝氏網路為基礎的電腦診斷測驗及動畫補救教學系統。提供學生在進行電腦診斷測驗後,能依其錯誤類型,即時進行電腦動畫補救教學。 本系統利用電腦來進行學生錯誤類型的分析,除了讓學生了解自我學習狀況,進而運用程式所提供補救的教學達到自學的效果之外,更希望能讓教師迅速了解學生學習問題所在,改進教學的方式及技巧。不再需要花太多的時間去診斷及進行補救教學,減輕教師的工作負擔,讓教與學都能發揮最大的成效。 本研究達成的成果如下: 一、運用貝氏網路模式,對學生的錯誤類型與能力指標的辨識率有很好的效果。 二、電腦化適性補救教學確可達到補救教學的效果。 關鍵字:貝氏網路、補救教學、數與量、能力指標

並列摘要


Abstract This paper integrates the opinions of the professors and mathematical teachers, the purpose is setting up a computerized diagnosing test and animated remedial instruction system, providing the student after computerized diagnosing test, according to the analysis of its mistake type, carry on the adaptability, immediately remediable teaching. In this system, we used the computer to analysis students’ mistake types. We hope to make students understand their own study problems, and then use the remedial instruction of computer procedure to improve their learning. So the system is used to assist teachers and students in evaluating and exploring the students’ learning process and outcomes. The results are as follows: 1. The results show that using Bayesian networks to diagnose the existence of mistake types and sub-skills in individual students can get good performance. 2. The progress of students are significant after taking the adaptive remedial instruction. Key words: Bayesian Networks, remedial instruction, figures and quantity, competence indicators.

參考文獻


白宏圖(2005)。以能力指標結構為基礎的電腦適性測驗編製及動畫補救教學之應用-以國小數學領域三年級數與量的認識為例。臺中健康暨管理學院訊工程學系研究所碩士論文。
許美華(2000)。國小二年級學童乘法解題策略變化之研究。屏東師範學院國民教育研究所碩士論文。
Cowell, R.(1999). Introduction to Inference for Bayesian Networks.In Jordan (1999), 9-26.
Cheng J., Bell D.& Liu, W.(1998). Learning Bayesian Networks from Data: An Efficient Approach Based on Information heory,Technical Report, University of Alberta.
David, H. (1995).A Tutorial on Learning with Bayesian Networks.Technical Report MSR-TR-95-06, Microsoft Research.

延伸閱讀