Social Practice with Data - Integrated Curricular Materials for Data Science

This project collaboratively develops interdisciplinary Data Science course materials that use real-world practice to knit together human, contextual, and ethical (HCE) learning with computational and inferential learning. Our course materials help students move through rigorous multidimensional analyses of compelling, socially relevant context -- for instance, on home valuations and property tax assessment in historically redlined Chicago, IL -- to highlight areas of data science that open up socially reflective practice and social justice. Course materials designed on this model are demonstrably engaging to students and help create a class environment where students' diverse identities and experiences are welcomed. Through the project, we show how instructors with different expertise can partner as equals to create and deliver integrated learning experiences that no single discipline can convey on its own. We also show that successfully teaching this integrated material at scale -- for instance, in the junior-level gateway class Data 100, with 1000+ students each semester -- requires changes in pedagogy and course staff (GSI/UGSI) training.