Nina Bailey (University of North Carolina); Vimal Rao, Carlos Chavez, & John Bartucz (University of Minnesota)
Many data science educators have embraced the empowering role that statistics and data analysis can have on society and sought to incorporate social justice into data science education. We posit that situating social justice learning objectives within data science curricula requires more than simply utilizing critical contexts - It requires unique considerations for the design of a learning environment, the interactions between the instructor and students, and the instructor’s role in facilitating interactions between students; It requires careful consideration of how social justice learning objectives interact with data science learning objectives, and how to navigate seemingly contradictory goals, such as seeing every person as an individual while also reasoning about distributions. Participants will interact with two panelists who have designed statistics and quantitative methods courses through the lens of race and gender. Discussions will focus on how to prepare to teach a fully integrated course centered on social justice initiatives.