Embracing Variability: Using Learning Sciences to Teach Introductory Statistics to Students with Diverse Math Preparation

Abstract

Introductory statistics classrooms increasingly serve students with wide variation in mathematical preparation, yet all are now expected to reason with data and models in an AI-rich world. Instructional design needs to consider this heterogeneity in students to prevent the widening equity gaps and ensure meaningful learning for all. This workshop draws on learning science research to explore how hands-on, embodied activities can support rigorous learning in classrooms that include students with strong mathematical backgrounds alongside those with anxiety, limited preparation, or uneven prior knowledge. Participants will explore paper-based and embodied activities that use physical representations of data, distributions, and models. We will examine how these activities function cognitively: enabling students to form connections that make abstract ideas make sense. Evidence from an in-site classroom experiment, along with analysis of instructional artifacts, will ground discussion of how these approaches can transform whole-class instruction to better support learning for all students.