🤖 AI Summary
This study addresses the challenge of effectively introducing Bayesian inference in introductory statistics courses while minimizing computational complexity and fostering active learning. To this end, the authors developed a classroom-ready instructional activity grounded in Process-Oriented Guided Inquiry Learning (POGIL), which employs two-way probability tables to guide students through manual calculations of conditional probabilities and belief updating, implemented via structured small-group roles. A reproducible Bayesian assessment framework was also created, with learning outcomes analyzed using bivariate generalized linear models. Empirical results demonstrate that this approach yields exam performance and instructional satisfaction comparable to traditional lecture-based methods, with consistent efficacy across majors, genders, and racial groups, thereby affirming its feasibility, inclusivity, and potential for broad adoption.
📝 Abstract
We introduce a Process Oriented Guided Inquiry Learning (POGIL)-style activity for teaching Bayesian reasoning in introductory statistics through conditional probability, Bayes' theorem, and belief updating. The activity is self-contained, uses hand-computable probabilities organized in two-way tables, and engages students in structured team roles. We evaluated the activity in four sections of an undergraduate introductory statistics course using a quasi-experimental comparison of POGIL-style and lecture-based instruction for a Bayes' theorem unit. Outcomes included student performance on Bayes' theorem final exam questions and satisfaction with instruction. We used a Bayesian bivariate generalized linear model to compare the two approaches while accounting for major type, gender, and race. The results indicated similar exam performance and similar probabilities of high satisfaction across instructional styles and demographic groups, with considerable uncertainty and no clear evidence of meaningful differences. These findings suggest that the POGIL-style activity performed comparably to lecture-based instruction for this unit while offering an active and classroom-ready way to introduce Bayesian reasoning without requiring difficult computation or simulation. We provide adaptable instructional materials and a reproducible Bayesian analytic framework for evaluating active learning innovations in introductory statistics. Our study supports the feasible inclusion of Bayesian reasoning in introductory courses and may help instructors considering active learning.