SCIP: a self-paced, community-based summer coding program creates community and increases coding confidence, lessons learned from the pandemic

Abstract

In 2020, many students lost summer opportunities due to the COVID-19 pandemic. We wanted to offer students an opportunity to learn computational skills and be part of a community while stuck at home. Because the pandemic created an unexpected research and academic situation, it was unclear how to best support students to learn and build community online. We used lessons learned from literature and our own experience to design, run and test an online program for students called the Science Coding Immersion Program (SCIP). In our program, students worked in teams for 8 hours a week, with one participant as the team leader and Zoom host. Teams worked on an online R or Python class at their own pace with support on Slack from the organizing team. For motivation and career advice, we hosted a weekly webinar with guest speakers. We used pre- and post-program surveys to determine how different aspects of the program impacted students. We were able to recruit a large and diverse group of participants who were happy with the program, found community in their team, and improved their coding confidence. We hope that our work will inspire others to start their own version of SCIP.

Using a decision tree to predict the number of COVID cases: a tutorial for beginners

“…In 2021 and 2022, we let students at San Francisco State University in the Biology Master's program and in the summer SCIP program [15] work through the module. We found that our students had limited prior knowledge of machine learning (see Figure 2, panel A), but after doing the tutorial, 71.8% of the students were interested in learning more (the remaining students indicated they were "maybe" interested in learning more, and one student out of 78 indicated they were not interested in learning more).…”

“…We programmed our tutorial in Python, which is one of the most popular coding languages [12,13]. It is considered a good language to learn for beginners because of its relative simplicity [14]. The tutorial does not require specialized knowledge in any particular biological concept and is designed to be a friendly introduction to the decision tree algorithm for students with no prior coding or machine learning knowledge.…”