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題名:教育政策與行政新計量系統分析應用-以臺灣十二年國民基本教育為例
作者:賴文宗
作者(外文):Wen-Tsung Lai
校院名稱:國立暨南國際大學
系所名稱:教育政策與行政學系
指導教授:吳柏林
吳明烈
學位類別:博士
出版日期:2015
主題關鍵詞:十二年國民基本教育模糊統計模糊時間序列區間模糊數無母數分析12-year basic educationfuzzy statisticfuzzy time seriesinterval fuzzy scorenonparametric analysis
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本文旨在針對臺灣實施十二年國民基本教育後之五個問題做實證研究,以提供教育當局參酌:一為,如何更有效鑑別學生的學習成就,以利學生學習成就比較?二為,為何會考考科設計特別重視數學科,到底數學科與其他學科之相關性為何?三為,為何會考一定要考數學科,是否基於諺語「數學為科學之母」考量,其實證之因果關係為何?四為,申請入學超額比序項目針對弱勢學生採特別加分,弱勢學生的學習成就真的比較低嗎?五為,高中入學之超額比序項目是否可參採在校成績?
本研究對象為立意抽樣臺灣中部一所國民中學,樣本為該所學校在97學年至102學年間六年全體1,922位畢業生。研究方法為模糊統計在學生成績的應用研究,統計方法包含使用區間模糊數、反模糊化、模糊相關區間、模糊相關、模糊均數、模糊權重、模糊時間序列、自迴歸函數、轉換函數及無母數分析等。
本研究獲得五項結論,結論一為:使用區間模糊數及反模糊化更能真實表示學生學習成就及提高比較之鑑別度;結論二為:學生在校數學學習成就與在校國文、英文、自然、社會學習成就間均具有關高相關以上強度;結論三為:學生在校數學學習成就與在校自然學習成就間有關「數學為科學之母」之因果關係解釋力極高;結論四為:弱勢學生之在校學習成就顯著低於一般學生之學習成就;結論五為:以學生在校成績來預測學生基本學力測驗成績之解釋力極高。
Empirical study conducted by the academia is required to resolve the 12-year basic education problem. The objective of this study is to resolve the five aforementioned research questions through innovative research and the application of the metering system.
This study first concentrates on the following five questions to conduct empirical study and provide future reference for the education authority. First, how can the learning achievement of students be accurately defined for effective identification? Second, why does the design of the national examination in the 12-year basic education specially attach importance to math subject? Third, math is the predecessor of science, but what is the empirical causal relationship between the two? Fourth, is the learning achievement of disadvantaged students lower for special bonus points? Fifth, why is it that school-based assessment cannot be considered to compare the sequence of senior high school admissions that exceed the quota?
Our samples comprise 1,922 Taiwanese graduates of the same junior high school which are purposive sampling during 2008–2013 in central Taiwan. The statistical methods apply the fuzzy theory, including IFSs, defuzzification, the correlation interval, fuzzy correlation, fuzzy mean scores, fuzzy weights scores, fuzzy time series, self-regression, the transfer function, and nonparametric analysis.
The results can resolve the research problems and contribute to the policy of 12-year basic education. First, using the IFSs of Definition 3.1 and the defuzzification function of Definition 3.2 can resolve the sorting problem when student scores exhibit low discrimination. Second, using the new model of fuzzy correlation with IFSs to assess the fuzzy correlation between the math subject and other subjects of the national examination shows significant differences. Third, using the transfer function of Definition 3.6 to evaluate the causal relationship between the math subject and science subject by using IFSs shows significant high explanatory power. Fourth, using the fuzzy mean scores of Definition 3.9 and the fuzzy weights of Definition 3.10 to assess the median between disadvantaged students’ learning achievement and general students’ learning achievement shows significant difference according to Wilcoxon rank-sum test. Fifth, using self-regression and fuzzy time series to assess the R-Sq. (adj) explanatory power of the five total scores, namely the Chinese, English, math, society, and science subject scores of school-based assessments, to the five total scores of the Basic Competency Test (BCTEST, henceforth) shows significant high explanatory power.
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