The Programme for International Student Assessment (PISA) has become the world’s largest comparative assessment of academic achievement. While hundreds of studies have examined the factors predicting student achievement in PISA, a comprehensive overview of the main predictors has yet to be completed. To address this gap, we conducted a systematic literature review of factors predicting mathematics performance in PISA. Guided by Bronfenbrenner’s ecological model of human development, we synthesized the findings of 156 peer reviewed articles. The analysis identified 135 factors that fall into five broad categories: individual student, household context, school community, education systems and macro society. The analysis uncovered seven factors that are consistently associated with math achievement in PISA. Student grade level and overall family SES (socio-economic status) are consistently positively associated with math achievement while five factors are consistently negatively associated with math achievement: student absenteeism and lack of punctuality, school repeating and dropout rate, school prevalence of students’ misbehavior, shortage of teachers and general staff, and student-centered instruction. Fourteen factors tend to be positively or negatively associated with math achievement. The explanatory power of many other factors, however, remain mixed. Explanations for this result include methodological differences, complex interactions across variables, and underlying patterns related to national-cultural context or other meso or macro-level variables. Implications for policy and research are discussed.

中文翻译：

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PISA 数学成绩预测因素：系统评价

国际学生评估计划（PISA）已成为世界上最大的学术成就比较评估。虽然数百项研究已经检验了预测学生 PISA 成绩的因素，但对主要预测因素的全面概述尚未完成。为了弥补这一差距，我们对预测 PISA 数学成绩的因素进行了系统的文献综述。在布朗芬布伦纳的人类发展生态模型的指导下，我们综合了 156 篇同行评审文章的研究结果。该分析确定了 135 个因素，分为 5 大类：学生个体、家庭背景、学校社区、教育系统和宏观社会。分析发现了与 PISA 数学成绩一致相关的七个因素。学生年级水平和整体家庭SES（社会经济地位）始终与数学成绩呈正相关，而五个因素始终与数学成绩呈负相关：学生缺勤和不准时、留级和辍学率、学生行为不端的学校发生率，教师和普通工作人员短缺，以及以学生为中心的教学。有十四个因素往往与数学成绩呈正相关或负相关。然而，许多其他因素的解释力仍然参差不齐。对此结果的解释包括方法论差异、变量之间复杂的相互作用以及与民族文化背景或其他中观或宏观变量相关的潜在模式。讨论了对政策和研究的影响。