By Amelia McNamara (University of St Thomas); Chelsey Legacy, Vimal Rao, Robert delMas, Andrew Zieffler (University of Minnesota); Matthew Beckman, Elle Basner Butler (Penn State University)
Computation is a mainstay of current statistical practice, and aspects of computing are included in many data science and statistics courses. While specific learning goals related to computing vary across courses, thoughtful introduction and integration of this content should benefit learners, regardless of audience or course goals. This poster presents a range of curricular decisions made by the poster’s authors in their own statistics and data science courses to prioritize computation as a learning goal. We share case studies of these decisions and lessons learned from our decades of combined experience teaching R, drawing on research from the cognitive and learning sciences, to outline practices that have been effective in our classrooms. We present these not as the idealized solution or set of decisions, but rather to demonstrate and provide other instructors with ideas and inspiration for how to thoughtfully integrate computing into their own courses.