Teaching Statistics and Data Science Collaboration via a Community of Practice

Abstract

Due to the applied nature of statistics and data science, many educators in these fields recognize the need to teach their students how to be effective interdisciplinary collaborators. Some prior research considers different approaches to teaching interdisciplinary collaboration skills. However, missing from this literature are the connections between teaching collaboration and education theory. Thus, there is a lack of understanding about why the various pedagogical approaches may be effective. In this descriptive study, we describe an approach to teaching interdisciplinary collaboration using a Community of Practice (CoP) and highlight connections between potentially reproducible elements of this approach and education theory that explains why this approach may be effective from the perspectives of both education and collaboration theory. Our results show that students and content-area experts recognize this approach to teaching statistical and data science collaboration to be effective. By grounding our methods for teaching statistics and data science collaboration skills in education theory, we focus attention on which aspects can be replicated in other contexts, why they work well, and how they can be improved. We recommend instructors intentionally create a CoP within their courses, encourage peer mentorship, and emphasize a growth mindset.