This Work-in-Progress paper in the Research Category uses a retrospective mixed-methods study to better understand the factors that mediate learning of computational modeling by life scientists. Key stakeholders, including leading scientists, universities and funding agencies, have promoted computational modeling to enable life sciences research and improve the translation of genetic and molecular biology high- throughput data into clinical results. Software platforms to facilitate computational modeling by biologists who lack advanced mathematical or programming skills have had some success, but none has achieved widespread use among life scientists. Because computational modeling is a core engineering skill of value to other STEM fields, it is critical for engineering and computer science educators to consider how we help students from across STEM disciplines learn computational modeling. Currently we lack sufficient research on how best to help life scientists learn computational modeling. To address this gap, in 2017, we observed a short-format summer course designed for life scientists to learn computational modeling. The course used a simulation environment designed to lower programming barriers. We used semi-structured interviews to understand students' experiences while taking the course and in applying computational modeling after the course. We conducted interviews with graduate students and post- doctoral researchers who had completed the course. We also interviewed students who took the course between 2010 and 2013. Among these past attendees, we selected equal numbers of interview subjects who had and had not successfully published journal articles that incorporated computational modeling. This Work-in-Progress paper applies social cognitive theory to analyze the motivations of life scientists who seek training in computational modeling and their attitudes towards computational modeling. Additionally, we identify important social and environmental variables that influence successful application of computational modeling after course completion. The findings from this study may therefore help us educate biomedical and biological engineering students more effectively. Although this study focuses on life scientists, its findings can inform engineering and computer science education more broadly. Insights from this study may be especially useful in aiding incoming engineering and computer science students who do not have advanced mathematical or programming skills and in preparing undergraduate engineering students for collaborative work with life scientists.
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Post-Doc Interviews in the Life Sciences: An Often-Overlooked Process that Is Susceptible to Bias
Synopsis Post-doctoral training is a critical career stage for researchers in the life sciences, yet interviewing for a post-doctoral position is largely an unregulated process. Without regulation, interviews are susceptible to unconscious biases that may lead to discrimination against certain demographic groups (e.g., women and minorities). Using data from an online survey of post-docs, we show that interview procedures for post-doctoral positions in the life sciences are correlated with several factors (e.g., candidate demographics) in ways that may bias the outcome of interviews. We discuss key components of interviews and suggest that conducting standardized, well-planned interviews that are less susceptible to unconscious biases may help increase the retention of women and under-represented minorities in the life sciences.
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- Award ID(s):
- 1711980
- NSF-PAR ID:
- 10180885
- Date Published:
- Journal Name:
- Integrative Organismal Biology
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2517-4843
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/iob/obz027
