Proposal Description

Statistical literacy and data reasoning have become foundational competencies for informed participation in contemporary societies, and their cultivation begins in school. Yet the quality of statistics and data science education depends fundamentally on the preparation and ongoing professional development of the teachers responsible for delivering it. This session brings together four empirical studies that examine, from complementary angles and across different educational levels, how teachers develop the knowledge, practices, and tools needed to foster statistical and data reasoning in their students. The contributions construct a multi-level panorama grounded in research conducted in diverse national contexts across the Americas and Europe.
Two contributions focus on early childhood and the early primary years. The first, conducted in Portugal, examines whether and how statistics and data science can be meaningfully integrated into pre-school curricula through developmentally appropriate, play-based, and inquiry-oriented experiences. Drawing on curricular analysis and practical illustrations aligned with Portuguese pre-school guidelines, the study argues that data-driven experiences support data literacy, critical thinking, numeracy development, and STEM readiness, and proposes design principles emphasizing authentic contexts, active exploration, and assessment approaches attentive to children's reasoning processes. The second study, conducted in Brazil, investigates how continuing professional development can support early childhood teachers in promoting statistical literacy through the mobilization of fundamental mental processes, including correspondence, comparison, classification, and seriation, and their intentional integration into everyday classroom experiences. Based on a qualitative case study involving four schools in southern Brazil, the research identifies pedagogical possibilities and challenges associated with introducing statistical ideas in the early years.
The third contribution, conducted in Chile, shifts to the early primary grades and examines the specialized knowledge for teaching statistics mobilized by K–3 teachers during a Lesson Study process focused on informal inferential reasoning. Using the Statistics Teacher Specialized Knowledge framework as an analytical lens, and incorporating a large language model as a complementary tool for qualitative coding, the study characterizes how pedagogical content knowledge articulates with statistical content knowledge in teachers' collaborative discourse. The findings reveal meaningful connections across subdomains of the framework and open questions about the potential and limitations of language models as analytical aids in educational research.
The fourth presentation addresses the K–8 level from a measurement perspective, reporting on the development and validation of an observational analytic tool designed to capture key data science practices in classroom instruction. Drawing on data science standards and curriculum frameworks and following a rigorous validation process, the study aims to operationalize the essential dimensions of data science education as observable classroom practices. The anticipated outcome is a validated instrument serving both as a formative assessment resource for educators and as an analytic tool for researchers across diverse national settings.
The discussant will synthesize the contributions, identify cross-cutting themes, and surface open questions relevant to researchers, teacher educators, curriculum designers, and policymakers.