Factors Impacting Mathematics Achievement in Shanghai and Pe
发布时间：2024-03-31 04:34
研究采用多层线性模型分析,模型的第一层和第二层分析不包含解释变量。该学校背景模型的目的在于检验秘鲁和上海的数学学科师生比、学校教育资源资料与学生数学成绩间的关系。结果表明,上海与秘鲁两者间在校生的数学平均成绩存在较大区别。学生背景模型用于检测秘鲁与上海学生的学前教育经历、性别、家庭经济社会文化地位与数学成绩的相关程度,组间相关分析发现秘鲁和上海一致。但两者对应的模型不同。所有秘鲁学生的背景变量都能显著预测数学成绩。而在上海,不同的学校之间,学前教育低于一年与学生数学成绩之间存在的相关程度也不同。研究还发现,上海学校数学教师师生比与学生数学成绩呈显著负相关,这表明数学师生比每增加一个单位将导致学生的数学成绩的下降。值得注意的是,秘鲁不同学校间的数学师生比存在较大差异。在2012年的PISA测试中,上海位列第1位,而秘鲁在参与的PISA测试的65个国家中排名最末位。本研究也检验了若干学生背景变量与其数学成就的相关程度,国家间的文化差异可能是预测变量在学生及学校这两个层面上产生不同影响的一种解释。此外,本研究证实了国家间差异的存在,适合预测某个国家学生数学成绩的模型不一定适用于别的国家。本研...
【文章页数】：90 页
【学位级别】：硕士
【文章目录】：
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
本文编号：3943455
【文章页数】：90 页
【学位级别】：硕士
【文章目录】：
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
本文编号：3943455
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