Learning Data Science in Elementary School Mathematics: A Comparative Curriculum Analysis

Autor/inn/en	Ow-Yeong, Yook Kit; Yeter, Ibrahim H.; Ali, Farhan
Titel	Learning Data Science in Elementary School Mathematics: A Comparative Curriculum Analysis
Quelle	In: International Journal of STEM Education, 10 (2023), Artikel 8 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Yeter, Ibrahim H.) ORCID (Ali, Farhan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s40594-023-00397-9
Schlagwörter	Foreign Countries; Statistics Education; Elementary School Mathematics; Mathematics Curriculum; Mathematics Achievement; Comparative Education; Achievement Tests; Elementary Secondary Education; International Assessment; Mathematics Tests; Outcomes of Education; Comparative Analysis; Singapore; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Ausland; Elementare Mathematik; Schulmathematik; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Vergleichende Erziehungswissenschaft; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Lernleistung; Schulerfolg; Singapur
Abstract	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. 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. 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. (As Provided).
Anmerkungen	Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01