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Factors Mediating Learning and Application of Computational Modeling by Life Scientists

机译：生命科学家介导计算模型学习和应用的因素

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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.
机译：该研究类别中的进行中论文使用回顾性混合方法研究来更好地理解生命科学家介导计算模型学习的因素。主要的利益相关者，包括领先的科学家，大学和资助机构，已经促进了计算建模，以使生命科学研究和将遗传和分子生​​物学高通量数据转化为临床结果的方法得到改善。缺乏高级数学或编程技能的生物学家为计算建模提供便利的软件平台已经取得了一些成功，但没有一个在生命科学家中得到广泛使用。由于计算建模是其他STEM领域的一项核心工程技术技能，因此对于工程和计算机科学教育者来说，考虑如何帮助跨STEM学科的学生学习计算建模至关重要。当前，我们缺乏有关如何最好地帮助生命科学家学习计算模型的足够研究.2017年，为了弥补这一空白，我们观察到了一个短格式的暑期课程，旨在为生命科学家学习计算模型。该课程使用了旨在降低编程障碍的模拟环境。我们使用半结构化面试来了解学生在上课时以及在课后应用计算模型时的体验。我们对完成课程的研究生和博士后进行了采访。我们还采访了在2010年至2013年期间参加该课程的学生。在这些过去的参加者中，我们选择了相等数量的具有和未成功发表结合了计算建模的期刊文章的访谈对象。这份进行中的论文运用社会认知理论来分析寻求计算建模培训的生命科学家的动机及其对计算建模的态度。此外，我们确定了重要的社会和环境变量，这些变量会影响课程完成后计算模型的成功应用。因此，本研究的结果可能有助于我们更有效地教育生物医学和生物工程专业的学生。尽管本研究的重点是生命科学家，但其发现可以更广泛地为工程和计算机科学教育提供信息。这项研究的见解在帮助不具备先进数学或编程技能的工程学和计算机科学专业的新生以及使本科工程学专业的学生准备与生命科学家开展合作方面特别有用。

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