利用融合策略結合多重學生模式之貝式網路適性學習系統研發-以複合圖形為例

Adaptive learning system based on Bayesian network using fusion strategy for combining multiple student models -using compound shape for an example

陳榮昌(chen jung chang)

指導教授 ： 劉湘川

亞洲大學/資訊學院/資訊工程學系碩士班/碩士(2007年)

摘要

本研究以國小六年級數學領域之複合圖形為例，探討以證據為中心的評量架構並結合多重學生模式之貝氏網路為推論工具的評量診斷模式，建置一套方便有效的診斷系統，並以Flash 製作補救教學動畫，希望藉由本系統讓學生可以接受個別化的診斷測驗，並給予適性化、即時化的電腦補救教學。期能同時達到評量、診斷、補救教學的功能。
研究結果發現：
一、結合多重學生模式之貝氏網路確實可以提高辨識率。
二、本系統確能診斷出學生所具備的錯誤概念，顯示本研究中結合多重學生模式之貝氏網路所建立的適性測驗效果是良好的。
三、本研究之電腦化適性診斷測驗確實可以取代紙筆測驗，並且達到省時省力之效。
四、經由電腦適性化補救教學活動後，大部分的受測學生的錯誤類型均降低，而所具備之子技能大致都有提升，顯示本研究之電腦適性化補救教學系統的確可以提升學生學習成效。
五、學生之錯誤概念發生情形與具備之子技能會隨著所在地區與班級老師之教學方法之不同而有所差異。

關鍵字

以證據為中心的評量架構 ； 多重貝氏網路 ； 電腦適性化補救教學

並列摘要

The main purpose of the research is to explore the educational assessment on the basis of Evidence-Centered Design（ECD） to build a convenient and effective diagnosis system. We use multiple Bayesian networks for modeling assessment data and identifying bugs and sub-skills in The “Compound Shape” of Mathematics in Grade 6. This research integrates the opinion of the experts, scholars and primary school teachers. Also, the multimedia computer is devised for Diagnostic Testing and computerizes adaptive remedial instruction with the system. Students can receive not only individual diagnostic tests. But adequate and in-time computerized adaptive remedial instruction. Evaluation Diagnosis and remedy can be achieved simultaneously.
The findings of this research are as follows:
1. Multiple Bayesian networks did enhance the recognition level.
2. The system could diagnose students’ errors, which shows that the adaptive test based on the multiple Bayesian networks was effective.
3. The computerized adaptive remedial instruction was testified to be able to replace written tests in a convenient and time-saving way.
4. After adopting computerized adaptive remedial instruction, the bugs of most students were reduced and their skills were improved. It revealed that the computerized adaptive remedial instruction did help enhance students’ learning effects.
5. The distribution of students’ bugs and skills varied with districts where the schools were located and the teachers’ teaching methods.

並列關鍵字

Evidence-Centered Design ； multiple Bayesian networks ； computerize adaptive remedial instruction

參考文獻

范瑞君(2006)。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~以六年級數學領域之『量與實測』相關指標為例。亞洲大學資訊工程學系碩士在職專班碩士論文，台中縣。

陳怡如、吳慧珉與黃碧雲，“電腦化適性診斷測驗之研究“，測驗統計年刊，第十二輯，上期，66-99 頁。

Almond,R.G.,Mislevy, Robert J. (1999) Graphical models and computerized adaptive testing. Applied Psychological Measurement,23(3):223-237.

Gable, R. A., Enright, B. E., and Hendrickson, J. M.(1991).Curriculum Based Assessment and Instruction in Arithmetic.Teaching Exceptional Children, 24(1), 6-9.

Kuo, B.C., Hsieh, T.Y., & Chang, Y.Y (2006). Combining Multiple Bayesian Networks for Modeling Students' Learning Bugs and Skills. The 7th International Conference on Intelligent Technologies, Taipei, Taiwan 13-15, December 2006