Mathematical literacy (ML) is considered central to the application of mathematical knowledge in everyday life and thus is found in many comparative international educational standards. However, there exists barely any evidence about predictors and outcomes of ML having a lasting effect on achievement in nonmathematical domains. We drew on a large longitudinal sample of N = 4001 secondary school students in Grades 5 to 9 and tested for effects of ML on later academic achievement. We took prior achievement in different domains (information and communication technology literacy, scientific literacy, reading comprehension, and listening comprehension), socioeconomic status, and gender into account and investigated predictive effects of math grade, mathematical self-concept, reasoning, and prior achievement on ML. Using structural equation models, we found support for the importance of integrating multiple predictors and revealed a transfer effect of ML on achievement in different school domains. The findings highlight the importance of ML for school curricula and lasting educational decisions.

中文翻译：

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数学素养的转移效应：综合纵向研究

数学素养 (ML) 被认为是数学知识在日常生活中应用的核心，因此可以在许多比较国际教育标准中找到。然而，几乎没有任何证据表明 ML 的预测因素和结果对非数学领域的成就有持久影响。我们抽取了 N = 4001 名 5 至 9 年级中学生的大型纵向样本，并测试了 ML 对以后学业成绩的影响。我们考虑了不同领域（信息和通信技术素养、科学素养、阅读理解和听力理解）、社会经济地位和性别的先前成就，并调查了数学成绩、数学自我概念、推理和先前成就的预测效果在 ML 上。使用结构方程模型，我们发现了对整合多个预测因素的重要性的支持，并揭示了 ML 对不同学校领域成就的转移效应。研究结果强调了机器学习对学校课程和持久教育决策的重要性。