- 期刊
- OpenAccess
發展多元評量模糊複合分數之初探
A Preliminary Study of Development of Fuzzy Composite Score for Multiple Assessments
摘要
隨著評量測驗的時代演進，傳統的單一標準化測驗已逐漸為多元評量所取代。然而，在多元評量強調評量工具、評量方式以及評量標準之多元性的前提下，教師如何依據多元化的評量結果進行複合分數的計算，並對學生的學習情形之良窳提出適當的建議，將是教學的首要工作，也是教育研究者所當深思的課題。本研究旨在利用模糊理論符合人類語意描述與思考邏輯之特性，捨棄線性且絕對性的傳統分數處理方式，改以非線性的模糊邏輯推理方式，發展一個整合多元評量分數的運算機制，經由該機制所運算所得的結果研究者稱其為“模糊複合分數(fuzzy composite score, FCS)”，而該機制則稱其為“FCS評分架構”。研究者並依據教學情境之可能性，設計一組研究模擬樣本分數，藉由模擬樣本分數為輸入，進行模糊複合分數之整合評分工作。經結果分析與討論可得知：利用本研究所發展之評分架構推論而得到的模糊複合分數，能夠與本研究之假設與目的相互吻合，並可看出本研究所提出之FCS評分架構對於不同量尺的評量分數之整合，具有其便利性與適切性。同時，研究者也對於專家資料庫的建置以及專業評分軟體之設計，提出相關後續研究之建議。相信以本研究為基礎，持續相關之後續研究，應該可以使模糊複合分數在實際教學評量上的實用性與普適性更向前邁進一步。
並列摘要
Multiple assessments have taken the place of single standardized test recently. Tools, forms, and standards are multiple in the process of assessments. How to integrate scores of multiple assessments into a composite score will be an important subject in educational research. The aim of this research was to propose an operating mechanism based on fuzzy theory to integrate scores of multiple assessments. The character of fuzzy theory was to process information vaguely, imprecisely, and uncertainly. The composite score was called ”fuzzy composite score, FCS” in this research. A simulated sample was used to test this fuzzy scoring frame. As the result shown, the operating mechanism agreed to the hypotheses and purpose of this research. It was convenient to integrate scores in different scales, too. Finally, some suggestions for future researches were proposed including how to design the professional software and build knowledge base of expert systems. It's believed that works based on this research will make FCS more close to the educational practice in the future.
參考文獻
被引用紀錄
延伸閱讀
- 孟令夫、林幸台、林克忠（2003）。手側化評量項目之再次探索性因素分析。職能治療學會雜誌，21()，45-55。https://doi.org/10.6594/JTOTA.2003.21.04
- 涂倫彰（2022）。宅配應用混合多準則決策方法於據點設立之評選〔碩士論文，淡江大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0002-1807202217410900
- 鍾愛蓮（2009）。模糊多目標非相關平行機台排程問題之解算〔碩士論文，元智大學〕。華藝線上圖書館。https://doi.org/10.6838/YZU.2009.00110
- 杜素豪、羅婉云、洪永泰（2009）。A Study of Survey Nonresponse Bias Using Propensity Score Adjustment。政治科學論叢，(41)，151-175。https://doi.org/10.6166/TJPS.41(151-175)
- Vinay, Kumar, M., Johri, P., & Kumar, R. (2014). Empirical Evaluation of Fuzzy Synthetic Based Framework for Multifaceted Component Classification and Selection. Research Journal of Applied Sciences, Engineering and Technology, 7(9), 1832-1836. https://www.airitilibrary.com/Article/Detail?DocID=20407467-201403-201506300027-201506300027-1832-1836