What works where? Identifying Pedagogical Practices to Optimize Data Literacy Learning in Singapore and America

• In Event: AERA Roundtable Session Sunday 7:45 am - Gold 3
  In Roundtable Session: International Perspectives on the Teaching and Learning of Mathematics

Abstract

Data literacy has become an increasingly important skillset. However, evidence shows declines in data literacy skills, while controversies remain about best practices in math education. This paper explores the concept of an “Optimal Learning Zone”, a combination of teaching practices and classroom climate that maximize students’ data literacy learning, in Singapore and the U.S. We leverage descriptive statistics and quasi-experimental methods to examine sources of within-country and between-country gaps in data literacy using PISA 2022 data. We find significant, curvilinear associations between data literacy skills and four classroom factors: disciplinary climate, teacher support, exposure to math concepts, and cognitive activation. Results suggest that the salience of various classroom features in predicting data literacy learning varies across national contexts.

Authors

• Fabian Barch, University at Buffalo - SUNY

• Jaekyung Lee, University at Buffalo - SUNY

• Ji-Won Son, University at Buffalo - SUNY

• Young Sik Seo, University at Buffalo - SUNY