Design Considerations for Facilitating Equitable Participation in an Ethical Data Science Course for High School Students

Abstract

The emergence of data science, Big Data Analytics, and other related methodologies have resulted in differential outcomes for people situated differently in society. Exacerbating these effects is the privilege hazard (D’Ignazio & Klein, 2020) that occurs when teams of data scientists are composed primarily of individuals from privileged backgrounds. Therefore, the purpose of this article is to understand how educators may support students from different backgrounds (both relatively privileged and marginalized) to participate equitably and meaningfully in ethical data science discussions in order to safeguard against the privilege hazard. To do this, we draw on the literature regarding STEM identity formation, use Cobb and Yackel’s (1996) framework for analyzing social norms for discourse in inquiry-based classrooms, and draw on Hodge and Cobb’s Cultural Participation Orientation towards developing an inclusive classroom environment. Finally, we describe the course design elements (task structures, participation structures, and discursive moves) that supported students’ equitable participation in ethical data science discussions (Sandoval, 2004).