Framing the Foundations of Data Science Learning with Young Children

Data increasingly shapes how people learn about the world, make decisions, and participate in society. Yet little is known about how young children develop foundational knowledge in data science or how early educators can support this learning during preschool. This project will address this gap by investigating how and under what conditions young students learn to: (1) ask questions that can be answered with data; (2) collect and organize information; (3) create different types of data representations; and (4) use evidence to answer questions. By identifying how children’s data science thinking develops in early childhood, this project will provide education scholars and practitioners with the foundational knowledge needed to promote teaching, boost learning, and advance student access to data science education from the earliest years. Findings will inform future curriculum development, professional learning, and education policy, while contributing to a stronger foundation for fostering data literacy and mathematical reasoning. This project will develop and empirically validate a Preschool Data Science Learning Trajectory, a developmental model that describes increasingly sophisticated ways young children engage in investigations with data. Building on prior research on preschool data collection and analysis, mathematics learning trajectories, statistical thinking, and early data science education, this study will examine how children use their foundational mathematics competencies (e.g., counting, sorting, classifying, comparing, and measurement) to support their participation in data science practices including question posing, data collection, representation, interpretation, and evidence-based reasoning. Researchers will conduct classroom teaching experiments and cognitive interviews with preschool children attending Head Start programs. Using an iterative research design, this project will articulate, test, and refine a developmental progression for early data science. Using mixed-methods analyses, results of this project will describe how young children move from simple interactions with data toward increasingly sophisticated understandings as they reason about representations and engage in investigations. The resulting trajectory will provide an empirically grounded framework for understanding early data science learning and identify instructional supports that promote children’s participation in data-focused inquiry. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.