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Ten simple rules for an inclusive summer coding program for non-computer-science undergraduates
Since 2015, we have run a free 9-week summer program that provides non-computer science (CS) undergraduates at San Francisco State University (SFSU) with experience in coding and doing research. Undergraduate research experiences remain very limited at SFSU and elsewhere, so the summer program provi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7470413/ https://www.ncbi.nlm.nih.gov/pubmed/32881872 http://dx.doi.org/10.1371/journal.pcbi.1007833 |
Sumario: | Since 2015, we have run a free 9-week summer program that provides non-computer science (CS) undergraduates at San Francisco State University (SFSU) with experience in coding and doing research. Undergraduate research experiences remain very limited at SFSU and elsewhere, so the summer program provides opportunities for many more students beyond the mentoring capacity of our university laboratories. In addition, we were concerned that many students from historically underrepresented (HU) groups may be unable to take advantage of traditional summer research programs because these programs require students to relocate or be available full time, which is not feasible for students who have family, work, or housing commitments. Our program, which is local and part-time, serves about 5 times as many students as a typical National Science Foundation (NSF) Research Experiences for Undergraduates (REU) program, on a smaller budget. Based on our experiences, we present 10 simple rules for busy faculty who want to create similar programs to engage non-CS HU undergraduates in computational research. Note that while some of the strategies we implement are based on evidence-based publications in the social sciences or education research literature, the original suggestions we make here are based on our trial-and-error experiences, rather than formal hypothesis testing. |
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