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題名 高中職及五專免試入學採計國中在校學科分數加權機制之研究
A study of adopting weighting schemes on academic performance in school as an access for senior high schools and junior colleges without entrance examinations
作者 戴岑熹
貢獻者 宋傳欽<br>譚克平
戴岑熹
關鍵詞 學生綜合學科能力表現分數
在校成績
等加權重
類主成分分析
類典型相關分析
the synthetic scores of students` in-school academic performance
in-school grade
identical weighted method
Principal Component Type of Analysis
Canonical Correlation Type of Analysis
日期 2010
上傳時間 5-Oct-2011 14:39:42 (UTC+8)
摘要 國中基測實施迄今已十年,但是各種多元管道仍以國中基測量尺分數作為分發篩選之重要參據,多元能力評量參採比重偏低,國中學生升學競爭壓力未得緩解。本研究透過數學與統計分析的工具,尋找採用學生在校成績的方法,希望能找出更好的方式來代表學生在校三年的學習現況與學習成果,以做為免試升學採計在校成績的參考與依據。

本研究主要目的是要探討如何取決各科在校成績的權重(也就是在每個科目的分數之前乘上一個加權比重係數),以求得一個新的合成變量(由數個科目分數組成的線性組合),並用這個新合成變量做為學生在校的“綜合學科能力表現分數”,代表學生在校三年的基本學習能力及程度。

研究方法運用主成份分析與典型相關分析的觀念,但因限制條件設定的範圍與傳統主成分分析及典型相關分析的要求不一致,因此,我們便將所用的研究方法命名為「類主成分分析」與「類典型相關分析」。

研究中,方法主要在比較「類主成分分析」、「主成分分析」、「類典型相關分析」、以及「典型相關分析」四種方法與一般學校常用的「等加權比重」算平均成績的方法之分別;了解這些不同加權機制對同一所學校內學生的學科加權平均分數之成績排名百分比結果,以及與基測排名結果的差異。

「類主成份分析」研究結果發現,各科學科成績中變異數大的科目將獲得較大的權重比例,成為主導學生加權平均成績中舉足輕重的科目。另外;運用「類典型相關分析法」所求得的典型相關係數,其結果與傳統典型相關分析法以及使用最佳數值分析軟體(GAMS)所得的典型相關係數完全相同。

本研究最重要的貢獻之一,是我們在「類典型相關分析法」中證明並推導出一個求得各科權重的公式,只要使用此公式代入簡單的MATLAB程式,其所得的權重結果與最佳化數值分析軟體(GAMS)所得的結果完全相同,但花費的計算時間及成本卻遠少於GAMS所需,是一個求權重極便捷的方法,讀者可以在本論文附錄7.5.2或政大應數系網站上下載此程式。本研究最後結論也發現,類主成份分析的變異解釋率是所有方法中較高的;與基測總分結果較相近的則是類典型相關分析所得的權重機制;而等加權方法所得的排名結果則與基測排名結果差異最小。
The BCTEST (Basic Competence Test) for junior high school students has been implemented for ten years, however, the screenings for a variety of entrance programs are still based on the scale scores of the BCTEST with a low proportion of multi-intelligence. Hence, the competitive entrance pressure for junior high school students remains un-relieved. In view of this, via mathematics and statistics, this study is to explore an alternative approach which can not only reflect students` in-school grade, their learning situations and achievements but also represent a reference for entering senior high schools and junior colleges without entrance examinations.

The purpose of this study is to determine the different weightings of five learning subjects (that is, multiply the score of each subject by a weighted coefficient) and acquire a new composite variable from the linear combinations of five learning subjects. Then, use this new composite variable as the synthetic score of students` in-school academic performance.

Principal Component Analysis and Canonical Correlation Analysis are used in this study. Due to inconsistent restraints, the other two approaches we use are based on the concept of previously mentioned methodologies and denominated Principal Component Type of Analysis and Canonical Correlation Type of Analysis.

In the study, we compare with the different results of Principal Component Analysis, Principal Component Type of Analysis, Canonical Correlation Analysis, Canonical Correlation Type of Analysis and identical weighted method to realize how these different weighted schemes affect the rankings of students from the same school on both their weighted in-school grade and scores of the BCTEST (Basic Competence Test).

The outcomes of Principal Component Type of Analysis show that subjects with greater variance acquire larger weightings and play a dominant role in weighted in-school grade. Moreover, the correlation coefficients of Canonical Correlation Type of Analysis are completely the same as the ones of Canonical Correlation Analysis and GAMS.

One of the most important contributions in this study is we have proven and derived a formula to acquire different weightings of five learning subjects by using Canonical Correlation Type of Analysis. The acquired weightings are completely the same as the ones of GAMS with less time consuming. Readers can download this program in appendix 7.5.2 or from the website of Department of Mathematical Sciences, National Chengchi University(NCCU). We have also found that, the explanation rate of variance obtained from Principal Component Type of Analysis is the highest; the weighted scheme of Canonical Correlation Type of Analysis is more similar to the scores of the BCTEST; the difference of the rankings between identical weighted method and the BCTEST is the smallest.
參考文獻 [1]郭添財、周憲章(2010,2月)。擴大高中職及五專免試入學方案政策評論。臺灣教育,661期,30-33。
[2]陳柏熹、邱佳民、曾芬蘭(2010)。高中職入學制度中在校成績採計校正方式之比較。教育科學研究期刊,55(2),115-139。
[3]楊思偉(2006)。推動十二年國民教育政策之研究。教育研究集刊,52(2),1-31。
[4]周祝瑛(2009)。邁向十二年國民基本教育--由繁化簡的高中、職免試入學方案。教育資料集刊,第四十二輯--2009各國中等教育,25-42。
[5]宋耀廷、周業太、吳佩嶼、林秀珊、曾芬蘭(2010)。高中職入學制度中在校成績採計校正方式之比較。教育科學研究期刊,55(2),73-113。
[6]周愚文(2010,2,22)。「擴大高中職及五專免試入學實施方案」政策形成分析。中等教育,61(1),114-122。
[7]賴幸妮(2010)。「擴大高中職及五專免試入學實施方案」之初探研究。南投文教,29期,72-75。
[8]呂金河(2005)。線性代數導論(第八版)。華泰文化
[9]呂金河(1994)。線性代數(第五版)。華泰文化
[10]林清山(1983)。多變項分析統計法--社會及行為科學研究適用。臺北市 : 台灣東華書局
[11]陳順宇(2004)。多變量分析。臺北市 : 華泰書局
[12]楊浩二(1995)。多變量統計方法。臺北市 : 華泰書局
[13]張建邦(1997)。多變量分析。臺北市 : 三民書局
[14]陳耀茂(1999)。多變量解析方法與應用。臺北市 : 五南圖書出版
[15]涌井良幸,涌井貞美(2009)。圖解多變量分析--透視資料本質的科學分析工具。臺北市 : 鼎茂圖書出版
[16]張春興(2007)。教育心理學 : 三化取向的理論與實踐。臺北市 : 臺灣東華書局
[17]黃學亮(1995)。機率學精修講義。文笙書局股份有限公司
[18]教育部, 台北市政府教育局, 高雄市政府教育局(2010) 99年國中畢業生多元進路宣導手冊
[19]教育部, 台北縣政府教育局(2010) 台北縣新北星99學年度推導高中職免試入學宣導手冊
[20]Anderson, T. W.(1984). An Introduction to Multivariate Statistical Analysis, 2nd ed., New York: John Wiley and Sons, Inc
[21]Stewart, J.(2003). Calculus, 5th ed., Belmont, Calif.: Thomson Brooks/Cole
[22]Brooke, A., Kendrick, D., and Meeraus, A.(1992). GAMS : A User`s Guide Release 2.25, Danvers, Massachusetts: Boyd and Fraser Publishing Company
[23]Kolman, B. and Hill, D. R.(2005). Introductory linear algebra -An applied first course. Upper Saddle River, N.J.: Pearson Prentice Hall
[24]Gulliksen, H.(1950). Theory of mental tests, New York: John Wiley and Sons, Inc
[25]教育部十二年國民基本教育資訊網http://140.111.34.179/index.php
[26]數學知識網站EPISTE MATH
http://episte.math.ntu.edu.tw/entries/en_lagrange_mul/index.html
描述 碩士
國立政治大學
應用數學系數學教學碩士在職專班
97972006
99
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097972006
資料類型 thesis
dc.contributor.advisor 宋傳欽<br>譚克平zh_TW
dc.contributor.author (Authors) 戴岑熹zh_TW
dc.creator (作者) 戴岑熹zh_TW
dc.date (日期) 2010en_US
dc.date.accessioned 5-Oct-2011 14:39:42 (UTC+8)-
dc.date.available 5-Oct-2011 14:39:42 (UTC+8)-
dc.date.issued (上傳時間) 5-Oct-2011 14:39:42 (UTC+8)-
dc.identifier (Other Identifiers) G0097972006en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/51314-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學系數學教學碩士在職專班zh_TW
dc.description (描述) 97972006zh_TW
dc.description (描述) 99zh_TW
dc.description.abstract (摘要) 國中基測實施迄今已十年,但是各種多元管道仍以國中基測量尺分數作為分發篩選之重要參據,多元能力評量參採比重偏低,國中學生升學競爭壓力未得緩解。本研究透過數學與統計分析的工具,尋找採用學生在校成績的方法,希望能找出更好的方式來代表學生在校三年的學習現況與學習成果,以做為免試升學採計在校成績的參考與依據。

本研究主要目的是要探討如何取決各科在校成績的權重(也就是在每個科目的分數之前乘上一個加權比重係數),以求得一個新的合成變量(由數個科目分數組成的線性組合),並用這個新合成變量做為學生在校的“綜合學科能力表現分數”,代表學生在校三年的基本學習能力及程度。

研究方法運用主成份分析與典型相關分析的觀念,但因限制條件設定的範圍與傳統主成分分析及典型相關分析的要求不一致,因此,我們便將所用的研究方法命名為「類主成分分析」與「類典型相關分析」。

研究中,方法主要在比較「類主成分分析」、「主成分分析」、「類典型相關分析」、以及「典型相關分析」四種方法與一般學校常用的「等加權比重」算平均成績的方法之分別;了解這些不同加權機制對同一所學校內學生的學科加權平均分數之成績排名百分比結果,以及與基測排名結果的差異。

「類主成份分析」研究結果發現,各科學科成績中變異數大的科目將獲得較大的權重比例,成為主導學生加權平均成績中舉足輕重的科目。另外;運用「類典型相關分析法」所求得的典型相關係數,其結果與傳統典型相關分析法以及使用最佳數值分析軟體(GAMS)所得的典型相關係數完全相同。

本研究最重要的貢獻之一,是我們在「類典型相關分析法」中證明並推導出一個求得各科權重的公式,只要使用此公式代入簡單的MATLAB程式,其所得的權重結果與最佳化數值分析軟體(GAMS)所得的結果完全相同,但花費的計算時間及成本卻遠少於GAMS所需,是一個求權重極便捷的方法,讀者可以在本論文附錄7.5.2或政大應數系網站上下載此程式。本研究最後結論也發現,類主成份分析的變異解釋率是所有方法中較高的;與基測總分結果較相近的則是類典型相關分析所得的權重機制;而等加權方法所得的排名結果則與基測排名結果差異最小。
zh_TW
dc.description.abstract (摘要) The BCTEST (Basic Competence Test) for junior high school students has been implemented for ten years, however, the screenings for a variety of entrance programs are still based on the scale scores of the BCTEST with a low proportion of multi-intelligence. Hence, the competitive entrance pressure for junior high school students remains un-relieved. In view of this, via mathematics and statistics, this study is to explore an alternative approach which can not only reflect students` in-school grade, their learning situations and achievements but also represent a reference for entering senior high schools and junior colleges without entrance examinations.

The purpose of this study is to determine the different weightings of five learning subjects (that is, multiply the score of each subject by a weighted coefficient) and acquire a new composite variable from the linear combinations of five learning subjects. Then, use this new composite variable as the synthetic score of students` in-school academic performance.

Principal Component Analysis and Canonical Correlation Analysis are used in this study. Due to inconsistent restraints, the other two approaches we use are based on the concept of previously mentioned methodologies and denominated Principal Component Type of Analysis and Canonical Correlation Type of Analysis.

In the study, we compare with the different results of Principal Component Analysis, Principal Component Type of Analysis, Canonical Correlation Analysis, Canonical Correlation Type of Analysis and identical weighted method to realize how these different weighted schemes affect the rankings of students from the same school on both their weighted in-school grade and scores of the BCTEST (Basic Competence Test).

The outcomes of Principal Component Type of Analysis show that subjects with greater variance acquire larger weightings and play a dominant role in weighted in-school grade. Moreover, the correlation coefficients of Canonical Correlation Type of Analysis are completely the same as the ones of Canonical Correlation Analysis and GAMS.

One of the most important contributions in this study is we have proven and derived a formula to acquire different weightings of five learning subjects by using Canonical Correlation Type of Analysis. The acquired weightings are completely the same as the ones of GAMS with less time consuming. Readers can download this program in appendix 7.5.2 or from the website of Department of Mathematical Sciences, National Chengchi University(NCCU). We have also found that, the explanation rate of variance obtained from Principal Component Type of Analysis is the highest; the weighted scheme of Canonical Correlation Type of Analysis is more similar to the scores of the BCTEST; the difference of the rankings between identical weighted method and the BCTEST is the smallest.
en_US
dc.description.tableofcontents 謝辭................................................i
中文摘要............................................ii
Abstract..........................................iii
目錄................................................iv
表目錄............................................viii
圖目錄...............................................x
第一章 緒論...........................................1
1.1研究動機...........................................1
1.2研究目的..........................................11
1.3研究問題及研究方法.................................13
1.4研究對象及研究限制.................................16
1.5研究流程..........................................18
1.6專有名詞解釋.......................................19
第二章 文獻探討.......................................27
2.1主成份分析.........................................27
2.2典型相關分析.......................................35
2.3Lagrange Multiplier Method.......................40
第三章 類主成份分析....................................41
3.1類主成份分析理論的基本假設...........................41
3.2類主成份分析中各學科最佳權重的探討與推導...............52
3.3實例的分析與結論....................................62
3.4相關係數矩陣在類主成份分析的問題探討..................76
3.5類主成份分析之結果歸納...............................80
第四章 典型相關分析.....................................81
4.1類典型相關分析理論的基本假設..........................81
4.2類典型相關分析各學科最佳權重的探討與推導................84
4.3類典型相關分析對於原始資料與標準化資料之影響探討.........90
4.4實例的分析與結論.....................................95
第五章 結果與討論.......................................110
5.1學生綜合學科能力表現分數不同權重取決方法結果比較.........110
5.2各種學生綜合學科能力表現分數排名的差異比較..............123
第六章 總結............................................132
參考文獻...............................................135
附錄..................................................138
zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097972006en_US
dc.subject (關鍵詞) 學生綜合學科能力表現分數zh_TW
dc.subject (關鍵詞) 在校成績zh_TW
dc.subject (關鍵詞) 等加權重zh_TW
dc.subject (關鍵詞) 類主成分分析zh_TW
dc.subject (關鍵詞) 類典型相關分析zh_TW
dc.subject (關鍵詞) the synthetic scores of students` in-school academic performanceen_US
dc.subject (關鍵詞) in-school gradeen_US
dc.subject (關鍵詞) identical weighted methoden_US
dc.subject (關鍵詞) Principal Component Type of Analysisen_US
dc.subject (關鍵詞) Canonical Correlation Type of Analysisen_US
dc.title (題名) 高中職及五專免試入學採計國中在校學科分數加權機制之研究zh_TW
dc.title (題名) A study of adopting weighting schemes on academic performance in school as an access for senior high schools and junior colleges without entrance examinationsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1]郭添財、周憲章(2010,2月)。擴大高中職及五專免試入學方案政策評論。臺灣教育,661期,30-33。zh_TW
dc.relation.reference (參考文獻) [2]陳柏熹、邱佳民、曾芬蘭(2010)。高中職入學制度中在校成績採計校正方式之比較。教育科學研究期刊,55(2),115-139。zh_TW
dc.relation.reference (參考文獻) [3]楊思偉(2006)。推動十二年國民教育政策之研究。教育研究集刊,52(2),1-31。zh_TW
dc.relation.reference (參考文獻) [4]周祝瑛(2009)。邁向十二年國民基本教育--由繁化簡的高中、職免試入學方案。教育資料集刊,第四十二輯--2009各國中等教育,25-42。zh_TW
dc.relation.reference (參考文獻) [5]宋耀廷、周業太、吳佩嶼、林秀珊、曾芬蘭(2010)。高中職入學制度中在校成績採計校正方式之比較。教育科學研究期刊,55(2),73-113。zh_TW
dc.relation.reference (參考文獻) [6]周愚文(2010,2,22)。「擴大高中職及五專免試入學實施方案」政策形成分析。中等教育,61(1),114-122。zh_TW
dc.relation.reference (參考文獻) [7]賴幸妮(2010)。「擴大高中職及五專免試入學實施方案」之初探研究。南投文教,29期,72-75。zh_TW
dc.relation.reference (參考文獻) [8]呂金河(2005)。線性代數導論(第八版)。華泰文化zh_TW
dc.relation.reference (參考文獻) [9]呂金河(1994)。線性代數(第五版)。華泰文化zh_TW
dc.relation.reference (參考文獻) [10]林清山(1983)。多變項分析統計法--社會及行為科學研究適用。臺北市 : 台灣東華書局zh_TW
dc.relation.reference (參考文獻) [11]陳順宇(2004)。多變量分析。臺北市 : 華泰書局zh_TW
dc.relation.reference (參考文獻) [12]楊浩二(1995)。多變量統計方法。臺北市 : 華泰書局zh_TW
dc.relation.reference (參考文獻) [13]張建邦(1997)。多變量分析。臺北市 : 三民書局zh_TW
dc.relation.reference (參考文獻) [14]陳耀茂(1999)。多變量解析方法與應用。臺北市 : 五南圖書出版zh_TW
dc.relation.reference (參考文獻) [15]涌井良幸,涌井貞美(2009)。圖解多變量分析--透視資料本質的科學分析工具。臺北市 : 鼎茂圖書出版zh_TW
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