Bridging the Research-Practice Gap in Statistics and Data Science Education: Co-design "Embodied" Programming Notebooks with Instructors Across Institutions

Icy Zhang

Students are increasingly expected to reason with data, code, and models. Yet many struggle with foundational concepts that are highly abstract and difficult to connect to real-world experiences. Although embodied pedagogies have shown promise for supporting conceptual understanding in these domains, they have rarely translated into scalable, classroom-ready instruction. To address this challenge, this project brings together researchers and instructors from community colleges, teaching-focused institutions, and research universities to co-design, implement, and evaluate embodied instructional materials embedded within interactive Jupyter notebooks. Grounded in theories of embodied cognition, these materials integrate physical actions (e.g., gesture, drawing, manipulation) with computational tools to make abstract statistical and data science concepts more concrete and interpretable. This work is guided by three interrelated research questions: (1) How can research–practice partnerships be designed to support instructors, given their local goals and constraints, in collaboratively designing, implementing, and evaluating embodied Jupyter notebooks and guided paper notes? (2) To what extent and in what ways do co-designed embodied materials improve student outcomes? (3) How can instructors with distinct goals co-design embodied interventions that are locally relevant yet contribute to a shared theory of change in statistics and data science education? Through iterative design cycles and embedded assessments, the project will generate evidence on both student learning outcomes and instructor adaptation processes. This project will produce actionable design principles and an emergent, scalable theory of change for embodied instruction in statistics and data science.

About Icy Zhang

Icy (Yunyi) Zhang is an Assistant Professor of Learning Sciences in the Department of Educational Psychology at the University of Wisconsin–Madison. She earned her Ph.D. in Psychology from the University of California, Los Angeles. Her research examines the cognitive and developmental processes that underlie learning, and how instruction can be designed to support deep, flexible understanding in complex domains, particularly in STEM education. Her work centers on identifying learning challenges as they emerge in authentic classroom contexts and designing interventions that are responsive to those challenges. She collaborates closely with instructors, especially in statistics and data science, to pinpoint concepts that students consistently struggle with, such as confidence intervals and randomization-based inference. Drawing on theories of embodied cognition and the learning sciences, Icy develops and evaluates instructional approaches grounded in real classroom environments. These include embodied instructional videos, interactive simulations, and in-person activities designed to scaffold students' conceptual understanding. Using mixed-method research, her work integrates experimental methods with course-based implementation, aiming to bridge the gap between cognitive theory and scalable educational practice. Through this work, Icy seeks to advance both theoretical understanding of learning processes and practical solutions for improving teaching and learning in complex domains.