Background Data literacy is increasingly important in today’s data-driven world. Students across many educational systems first formally learn about data in elementary school not as a separate subject but via the mathematics curriculum. This experience can create tensions in the priorities of learning and assessment given the presence of other foundational mathematics content domains such as numbers, algebra, measurement, and geometry. There is a need to study data literacy in comparison to these other content domains in elementary mathematics. To address this need, we developed a methodology motivated by thinking curriculum theory and aligned with international assessment framework, for comparative analysis across mathematics content domains. This methodology examined increasing levels of cognitive domains from knowing to applying to reasoning across mathematics content domains. Intended, assessed, and attained curricula were analyzed using Singapore as a case study, combined with broader comparisons to attainments in four East Asian countries in TIMSS, an international large-scale assessment. Results We found that learning in the data domain had very limited coverage in intended and assessed curricula in Singapore. However, compared to other mathematics content domains, the data curriculum placed heavier emphasis on higher-order cognitive domains including the use of generally difficult mixed data visualizations. This demanding curriculum in Singapore was associated with the highest attainment in the data domain among average 4th grade Singaporean students relative to students in four East Asian countries in TIMSS, as analyzed by quantile regression. However, lower-performing Singaporean students at the 10th percentile generally did not outperform their East Asian peers. We further found very limited applications of data in other mathematics domains or cross-domain learning more generally. Conclusion Our study offers a comparative analysis of the data curriculum in elementary school mathematics education. While the data curriculum was cognitively demanding and translated to very high average attainments of Singaporean students, the curriculum did not equally help weaker Singaporean students, with implications on current discourse on equity–excellence trade-off in science, technology, engineering, and mathematics (STEM) education. Our study further highlights the lack of cross-domain learning in mathematics involving data. Despite the broad applicability of data science, elementary school students’ first formal experience with data may lack emphasis on its cross-domain applications, suggesting a need to further integrate data skills and competencies into the mathematics curriculum and beyond.

The aim of this study was to examine how achievement varied within and between schools at different grade levels, its long-term trends within and across multiple countries. We used science achievement data from five cycles of Trends in International Mathematics and Science Study (TIMSS) from 2003 to 2019 involving nine countries from Asia, Europe, and the United States. Employing exploratory data mining methods of variance decomposition, correlation analysis, and Gaussian mixture modeling of data distributions, we found the following: First, between-school variances generally remained consistent across two decades, suggesting that inequality between schools has not increased over time. Second, between-school variances were relatively small for elementary grade level but increased at secondary grade level, though marginally even for countries with early between-school tracking. Third, higher-achieving schools tended to have more equal student achievement levels than lower-achieving schools, lending within-country support for the “virtuous” efficiency-equality trade-off. We further found that reduced equality within lower-achieving schools was associated with bimodality in achievement distribution. Overall, there is little evidence of inequality across schools changing over time. However, there may be evidence of increased inequalities associated with student subpopulations, particularly in lower-achieving schools, with implications on classroom instruction and school cohesion.