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<p>This study aims to identify the key factors and pathways influencing students’ mathematics achievement in China, drawing on a decade of large-scale regional quality monitoring data. The research addresses the question of how cognitive, motivational, emotional, instructional, and institutional variables jointly affect mathematics performance across individual, family, school, and system levels. Using Bronfenbrenner’s ecological systems framework, the study synthesizes findings from 28 empirical projects conducted in multiple provinces and districts. Quantitative analyses, including structural equation modeling and mediation testing, reveal two dominant mechanisms: the affective–motivational chain (interest → self-efficacy → reduced anxiety → achievement) and the cognitive–strategic chain (cognitive activation → metacognitive strategy → achievement). Together, these pathways explain variations in performance and highlight how emotional engagement and cognitive regulation interact to shape learning outcomes. The results provide a coherent empirical overview of the multilevel determinants of mathematics achievement in China and offer a foundation for future longitudinal, comparative, and AI-supported assessment research.</p>

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Factors and Pathways Shaping Mathematics Achievement in China: Evidence from a Decade of Regional Quality Monitoring

  • 期刊论文

  • 发表日期：

    2025年12月2日

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  • Ruisi Li,
  • Chunxia Qi

摘要

This study aims to identify the key factors and pathways influencing students’ mathematics achievement in China, drawing on a decade of large-scale regional quality monitoring data. The research addresses the question of how cognitive, motivational, emotional, instructional, and institutional variables jointly affect mathematics performance across individual, family, school, and system levels. Using Bronfenbrenner’s ecological systems framework, the study synthesizes findings from 28 empirical projects conducted in multiple provinces and districts. Quantitative analyses, including structural equation modeling and mediation testing, reveal two dominant mechanisms: the affective–motivational chain (interest → self-efficacy → reduced anxiety → achievement) and the cognitive–strategic chain (cognitive activation → metacognitive strategy → achievement). Together, these pathways explain variations in performance and highlight how emotional engagement and cognitive regulation interact to shape learning outcomes. The results provide a coherent empirical overview of the multilevel determinants of mathematics achievement in China and offer a foundation for future longitudinal, comparative, and AI-supported assessment research.