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Computational thinking (CT) involves breaking a problem into smaller components and solving it using algorithmic thinking and abstraction. CT is no longer exclusively for computer scientists but for everyone. While CT does not necessarily require programming, learning programming to enhance CT skills at a young age can help shape the next generation of children with knowledge that can help them succeed in our technological world. In order to produce teachers who are able to incorporate programming and CT into their future classrooms, we created an introductory Computer Science course (CS0) targeting future K-8 STEM teachers yet open to any student to enroll and learn computer science. We used a mixed-methods approach, examining both quantitative and qualitative data based on self-reported surveys, classroom artifacts, and focus groups from four semesters of data. We found that after taking the course, students’ self-efficacy in CT increased and while education students initially had lower confidence in their computing abilities than computer science students in the course, by the end of the semester there were no differences in their perceived and actual coding abilities when compared with computer science students. Furthermore, education students had many ideas on how to incorporate similar projects into their own future classrooms.
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Factors Mediating Learning and Application of Computational Modeling by Life Scientists
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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- Award ID(s):
- 1830802
- PAR ID:
- 10110631
- Date Published:
- Journal Name:
- 2018 IEEE Frontiers in Education Conference (FIE)
- Page Range / eLocation ID:
- 1 to 5
- 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
- Conference Paper:
- https://doi.org/10.1109/FIE.2018.8659328
