Beth Chance (Cal Poly - San Luis Obispo), Karen McGaughey (Cal Poly - San Luis Obispo), Soma Roy (Cal Poly - San Luis Obispo), Todd Swanson (Hope College), Nathan Tintle (University of Illinois - Chicago) and Jill VanderStoep (Hope College)
Abstract
Introductory statistics education continues to evolve, from the integration of data science principles and practices, to leveraging improved understanding of statistical concepts in intermediate courses across disciplines. In this workshop, we offer a unifying strategy for teaching statistical thinking and data-sense, across common data scenarios seen in a first and/or second course in statistics. We will model classroom-ready activities, developed as part of an NSF-funded project, that empower students to think statistically from day one. Our approach focuses on engaging students with genuine research studies, in which every activity utilizes the statistical investigation process. Data visualization, multivariable thinking, and simulation-based inference are the bedrocks of the activities. The activities are GAISE-compliant and incorporate best teaching practices such as active/interactive-learning, use of accessible technology (e.g., applets), and scaffolded discovery-based learning. This workshop will give teachers of introductory and intermediate statistics courses the opportunity to play the role of student and think critically about how the activities can be adapted and used in the classroom. Teaching tips and assessment ideas will be shared so that teachers are prepared to update their models of teaching statistics in fall 2025.