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Abstract:
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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. In this talk, we present a range of curricular decisions we made in our own statistics and data science courses to prioritize computation as a learning goal. We share case studies of specific decisions we have made introducing R coding to students that draw on research from the cognitive and learning sciences, and outline practices that have been effective in our classrooms. While these decisions and lessons learned come from our decades of combined experience teaching R alongside statistics, we present them not as the idealized set of decisions. Rather, we seek to demonstrate and provide other instructors with ideas and inspiration for how to thoughtfully integrate computing into their own courses, regardless of syntax or programming language.
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