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<p>The COVID-19 pandemic has profoundly affected academic performance, intensifying existing disparities among students from lower socioeconomic status (SES) backgrounds, especially in the subjects mathematics and reading. This study examines the influence of SES on scientific literacy—a critical competency for informed participation in an increasingly science-driven society. Employing linear models and meta-analytic techniques, we analyzed data from the 2018 and 2022 PISA assessments across 69 participating countries (<i>N</i> = 1,060,311). Findings reveal a positive association between SES and scientific literacy (<i>ß</i> = .28, CI[.25, .31]). Notably, a significant interaction between SES and PISA cycle indicates that educational inequalities widened during the pandemic period (<i>ß</i> = .06, CI[.03, .09]). These results contribute to a deeper understanding of how socioeconomic factors shaped learning outcomes during COVID-19 and introduce a novel modelling approach for analyzing effects across two assessment cycles in large-scale education data.</p>

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Socioeconomic status and scientific literacy: expanding educational inequalities during the COVID-19 pandemic—insights from PISA 2018 and 2022

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Tamara Kastorff,
  • Jörg Henrik Heine

摘要

The COVID-19 pandemic has profoundly affected academic performance, intensifying existing disparities among students from lower socioeconomic status (SES) backgrounds, especially in the subjects mathematics and reading. This study examines the influence of SES on scientific literacy—a critical competency for informed participation in an increasingly science-driven society. Employing linear models and meta-analytic techniques, we analyzed data from the 2018 and 2022 PISA assessments across 69 participating countries (N = 1,060,311). Findings reveal a positive association between SES and scientific literacy (ß = .28, CI[.25, .31]). Notably, a significant interaction between SES and PISA cycle indicates that educational inequalities widened during the pandemic period (ß = .06, CI[.03, .09]). These results contribute to a deeper understanding of how socioeconomic factors shaped learning outcomes during COVID-19 and introduce a novel modelling approach for analyzing effects across two assessment cycles in large-scale education data.