3B: Positioning students in data science classrooms (Room 109)

Abstract

This session takes a deep dive into how students can be “positioned” as data scientists in secondary and undergraduate data science classrooms. We will introduce the positioning theory (Harré & van Langenhove, 1999) and think together about our interpersonal communication with students and its implication for facilitating an inclusive learning environment. The goals for the sessions are: a) gaining an initial understanding of positioning theory and its relevance to teaching statistics and data science, b) noticing multiple ways students are positioned during data science lessons in video excerpts, and c) co-developing ways to purposefully position students while highlighting their strengths and assets. This session connects with the sub-theme of “helping teachers communicate with students” to promote inclusivity in statistics and data science classrooms. When teachers facilitate data investigation activities (e.g., framing questions, exploring data, proposing actions), they also help students build relationships with the disciplines of statistics and data science. These interpersonal communications occur in subtle forms; thus, they require careful and focused attention. This session will pay close attention to this social and interpersonal aspect of “Communicating with/about Data” with eyes on developing students’ disciplinary identities and belonging associated with statistics and data science.