Exploration of factors affecting Australian students' mathematics grades: a multiple regression analysis based on PISA 2022 data

探究影响澳大利亚学生数学成绩的因素:基于PISA 2022数据的多元回归分析

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Abstract

INTRODUCTION: Currently, PISA (Programme for International Student Assessment) Mathematics Grades worldwide is declining, while Australia students' performance shows an upward trend. To promote mathematics education in Australia and share educational experiences, this study explores factors that impact Australia's PISA mathematics grades significantly, and identifies ways to improve mathematics performance. Guided by Bronfenbrenner's ecological systems theory, this study develops a dual-layer nested ecosystem model, including home context, information resources, personal feature, school environment, math teaching and learning. METHODS: Data is from PISA 2022 datasets. The independent variables are also divided into five categories according to the theoretical framework. There are 33 variables and 6,386 pieces of data. This study uses SPSS to conduct multiple regression analysis. In this study, predictors are categorized into five models, adding one influencing factor type to each model one by one. RESULTS: The factors in model 5 explain 51.1% of math grade changes. Home context has the strongest explanatory power, it explains 19.9% math grade changes. Home possession (β = 0.304) and ESCS (β = 0.266) benefit math performance. Math teaching and learning explains 17.2% of math grade changes. Mathematics self-efficacy: Formal and applied mathematics (MATHEFF) is more influential (β = 0.376). DISCUSSION: This study provides meaningful implications for identifying key determinants of mathematics education outcomes, informing evidence-based policy refinement, and enhancing instructional practice design. The findings offer actionable insights for stakeholders seeking to optimize mathematics learning ecosystems. To improve math achievement, Math education resource equity and scientific math teaching content are important.

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