研究采用多層線性模型分析,模型的第一層和第二層分析不包含解釋變量。該學(xué)校背景模型的目的在于檢驗(yàn)秘魯和上海的數(shù)學(xué)學(xué)科師生比、學(xué)校教育資源資料與學(xué)生數(shù)學(xué)成績間的關(guān)系。結(jié)果表明,上海與秘魯兩者間在校生的數(shù)學(xué)平均成績存在較大區(qū)別。學(xué)生背景模型用于檢測(cè)秘魯與上海學(xué)生的學(xué)前教育經(jīng)歷、性別、家庭經(jīng)濟(jì)社會(huì)文化地位與數(shù)學(xué)成績的相關(guān)程度,組間相關(guān)分析發(fā)現(xiàn)秘魯和上海一致。但兩者對(duì)應(yīng)的模型不同。所有秘魯學(xué)生的背景變量都能顯著預(yù)測(cè)數(shù)學(xué)成績。而在上海,不同的學(xué)校之間,學(xué)前教育低于一年與學(xué)生數(shù)學(xué)成績之間存在的相關(guān)程度也不同。研究還發(fā)現(xiàn),上海學(xué)校數(shù)學(xué)教師師生比與學(xué)生數(shù)學(xué)成績呈顯著負(fù)相關(guān),這表明數(shù)學(xué)師生比每增加一個(gè)單位將導(dǎo)致學(xué)生的數(shù)學(xué)成績的下降。值得注意的是,秘魯不同學(xué)校間的數(shù)學(xué)師生比存在較大差異。在2012年的PISA測(cè)試中,上海位列第1位,而秘魯在參與的PISA測(cè)試的65個(gè)國家中排名最末位。本研究也檢驗(yàn)了若干學(xué)生背景變量與其數(shù)學(xué)成就的相關(guān)程度,國家間的文化差異可能是預(yù)測(cè)變量在學(xué)生及學(xué)校這兩個(gè)層面上產(chǎn)生不同影響的一種解釋。此外,本研究證實(shí)了國家間差異的存在,適合預(yù)測(cè)某個(gè)國家學(xué)生數(shù)學(xué)成績的模型不一定適用于別的國家。本研...

【文章頁數(shù)】：90 頁

【學(xué)位級(jí)別】：碩士

【文章目錄】：
ACKNOWLEDGEMENT 摘要 Abstract Chapter One INTRODUCTION
1.1 Introduction to the Study
1.2 Background to the Study
    1.2.1 Key Features of PISA 2012
1.3 Statement of Problem
1.4 Purpose of the Study
    1.4.1 Research Questions
1.5 Significance of the Study
1.6 Rationale for choosing Peru and Shanghai-China
1.7 Conceptual Framework for Student and School level Factors
1.8 Education Production Function (EPF)
    1.8.1 International Evidence on Education Production Functions
    1.8.2 The Treatment of Endogenous Variables
1.9 Education and Economic Overview in Peru-Latin America and Shanghai-China
    1.9.1 Economic Overview of Peru
    1.9.2 Economic Overview of China
    1.9.3 The Trends in Peru Education System
    1.9.4 The Trends in Shanghai-China Education System Chapter Two REVIEW OF RELATED LITERATURE
2.1 Programme for International Student Assessment (PISA)
    2.1.1 The PISA Contextual Framework
2.2 Studies on Student and School-level Characteristics
    2.2.1 Gender Differences in Math Performance
    2.2.2 Preschool Education Attendance
    2.2.3 Students' Family Socioeconomic Status
    2.2.4 Math Teacher-Student Ratio
    2.2.5 Quality of School Educational Resources
2.3 Cultural Models of Education Chapter Three RESEARCH METHODOLOGY
3.1 Research Design
3.2 Variables for the Study
3.3 Sampling Design and Data Sources
3.4 Hierarchical Linear Modeling (HLM)
3.5 Rationale for using the HLM for the present Research
    3.5.1 Aggregation bias
    3.5.2 Misestimated standard errors
    3.5.3 Heterogeneity of regression
3.6 Data Analysis
    3.6.1 Models in the Study
3.7 Steps to be taken to Analyze the Data using HLM Chapter Four RESULTS
4.1 Sampling Procedure and Data Origin
4.2 Results for Peru
    4.2.1 Descriptive Statistics
    4.2.2 Unconditional Model
    4.2.3 Conditional Model
    4.2.4 The Final Model
4.3 Results for the Shanghai
    4.3.1 Descriptive Analysis
    4.3.2 Unconditional Model
    4.3.3 The Conditional Model
    4.3.4 The Final Model
4.4 Conclusion Chapter Five DISCUSSION
5.1 PURPOSE
    5.1.1 Review of Method
5.2 Results
    5.2.1 Unconditional Model
    5.2.2 Student Background Model
    5.2.3 School Background Model
    5.2.4 What are the Differences between the Two Countries?
5.3 Trustworthiness, Reliability and Validity
5.4 Limitations
5.5 Implications
5.6 Future Research
5.7 Recommendations References APPENDICES
Appendices A
Appendices B



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