Abstract
While quantitative analytical skills have always been a part of modern biomedical training, the big data revolution and digital research environment have increased the importance of computational approaches for biomedical graduate education. To address this growing need, Ph.D. programs have explored ways to integrate quantitative training into their existing curricula. However, these attempts have been hindered by limitations on total instructional time, faculty perceptions, and scalability. Here, we describe a flipped approach that combined a preexisting online course with group problem solving sessions to effectively and efficiently teach biomedical Ph.D. students key concepts in the use of the Python programming language for research. Following the COVID-19 related shutdowns in March 2020, we successfully adapted this approach to an all-online version where the formerly in-person problem-solving sessions occurred in small groups over Zoom. We found that students in both in-person and remote flipped formats showed increased confidence using Python and related this to their thesis research. Following the shift to the fully remote format, the lack of a physically present instructor seemed to increase students' reliance on their classmates, which in turn promoted peer learning and support. This flexible, scalable approach to computational training may address the needs of many biomedical Ph.D. programs.