The purpose of this study is to examine the characteristics of US schools associated with two measures of scientific literacy (content knowledge and “procedural and epistemic” knowledge) using the 2015 Programme for International Student Assessment (PISA) data. Because outcomes are nested within students, and students within schools, a multivariate three‐level modeling method was employed. About 21% of the total variance in science performance lies between schools, indicating that school characteristics are important in predicting scientific literacy. The results revealed clearly significant and positive relationships of the student‐level variables of grade, enjoyment, motivation, and economic/social/cultural status (ESCS) with both measures of science literacy, after controlling for school factors. A significant gender difference is seen in science content knowledge in favor of males. At the school level, the results from the full model suggest that school ESCS, climate, and school type are significant predictors of all students' performance. Surprisingly, school size, teaching experience, professional development (PD) participation, and science‐specific resources are not found to contribute significantly to achievement. Considerable variability is also evident among schools on student performance in both science knowledge domains as a result of school variables. For both low‐ and mid–high‐achieving students, the most significant school factor is school mean ESCS. PD participation and school climate are significantly associated with student performance only for the average and high‐performing groups. This paper can inform policymakers, researchers, and educators on how US schools can be supported to improve science learning.

中文翻译：

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仔细研究美国的学校：科学素养与哪些特征有关？使用PISA 2015进行多元多层次分析

这项研究的目的是使用2015年国际学生评估计划（PISA）数据，考察与两种科学素养测度（内容知识和“过程和认知知识”知识）相关的美国学校的特征。由于结果嵌套在学生内部，而学生嵌套在学校内部，因此采用了多变量三级建模方法。科学表现的总体差异中约有21％位于学校之间，这表明学校的特征对于预测科学素养至关重要。结果显示，在控制了学校因素之后，学生水平的年级变量，享受程度，学习动机和经济/社会/文化状况（ESCS）变量与科学素养的两种衡量指标之间均存在显着且正相关的关系。在科学内容知识方面，男性明显偏爱男性。在学校一级，完整模型的结果表明，学校的ESCS，气候和学校类型是所有学生成绩的重要预测指标。令人惊讶的是，未发现学校规模，教学经验，专业发展（PD）参与度以及特定于科学的资源对成就有重大贡献。由于学校变量的不同，学校在两个科学知识领域的学生表现也存在相当大的差异。对于成绩低下和成绩中等的学生，最重要的学校因素是学校平均ESCS。仅对于中等水平和高水平的群体，PD参与度和学校氛围与学生表现显着相关。本文可以告知政策制定者，