探索影响澳大利亚学生数学成绩的因素:基于PISA 2022数据的多重回归分析
Frontiers in psychology
|October 17, 2025
概括
澳大利亚学生澳大利亚学生
科学领域:
- 教育心理学教育心理学
- 数学教育教育 数学教育
- 教育中的社会经济因素
背景情况:
- 国际学生评估计划 (PISA) 数学成绩的全球下降与澳大利亚的上升趋势形成鲜明对比.
- 需要确定澳大利亚学生数学表现的关键驱动因素,以便针对性干预.
研究的目的:
- 探索影响澳大利亚PISA数学成绩的重要因素.
- 确定可行的策略,以提高澳大利亚学生的数学表现.
主要方法:
- 利用PISA 2022数据集,包括33个变量和6386个数据点.
- 采用Bronfenbrenner的生态系统理论来开发一个双层嵌套生态系统模型.
- 使用SPSS进行多重回归分析,将预测因子分类为五种顺序模型.
主要成果:
- 综合模型解释了51.1%的数学成绩差异.
- 家庭环境成为最强的预测因素,占差异的19.9%,房屋占有和ESCS显示出积极的影响.
- 数学自我效能 (MATHEFF) 在数学教学和学习领域具有很大的影响力,解释了17.2%的差异.
结论:
- 研究结果强调了家庭环境和自我效能在数学成绩中的关键作用.
- 强调公平获得数学资源和科学教学方法的重要性.
- 为完善教育政策和教学实践提供基于证据的见解,以优化学习生态系统.
更多相关视频
10:26Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
4.4K
07:32Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity
Published on: February 10, 2016
9.8K
相关概念视频
Multiple Regression
3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Correlations
35.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.7K
Regression Toward the Mean
6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
Microsoft Excel: Regression Analysis
1.5K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
To perform regression...
1.5K
Reliability and Validity
13.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
13.7K
Regression Analysis
8.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.0K
