Active learning in post-secondary statistics and data sciences teaching: Lesson-level moments and course-level alternative models

Statistics and data science post-secondary education have relied heavily on traditional lectures (“chalk and talk” or “slides”). However, newer pedagogies could increase student engagement and learning. Using thematic analysis, we provide a scholarly review of the literature to summarize and synthesize research recommendations related to active learning in this area. We focus on recent research (2011-2022), exploring the ways active learning supplements or replaces traditional classroom instructional practices and its subsequent implications on learning. We found a distinguishing feature between models of active learning: instructors employ either a “lesson-level moments” model where segments of active learning are integrated within traditional instruction, or a “course-level alternative” model where active learning replaces a traditional approach; these two models can be viewed as representing two points on the active learning continuum. Rather than any one model being viewed as superior, there was a strong consensus that the simple implementation of any form of active learning may have positive impacts. Despite these benefits, resources may be lacking to support instructors in implementing and evaluating active learning strategies. Consequently, we conclude by discussing general considerations for active learning and assessment practices in statistics and data sciences education, implications for classroom instruction, and further research opportunities.