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  • 标题:Assessing the impact of introductory physics for the life sciences on students’ ability to build complex models
  • 本地全文:下载
  • 作者:Benjamin D. Geller ; Maya Tipton ; Brandon Daniel-Morales
  • 期刊名称:Physical Review Physics Education Research
  • 电子版ISSN:2469-9896
  • 出版年度:2022
  • 卷号:18
  • 期号:1
  • 页码:010131
  • DOI:10.1103/PhysRevPhysEducRes.18.010131
  • 语种:English
  • 出版社:American Physical Society
  • 摘要:A central goal of introductory physics for the life sciences (IPLS) is to prepare students to use physics to model and analyze biological situations, a skill of increasing importance for their future studies and careers. Here we report our findings on life science students’ ability to carry out a sophisticated biological modeling task at the end of first-semester introductory physics. Some students were enrolled in a standard course (N=34), and some in an IPLS course (N=61); both courses were taught with active learning, used calculus, and included the same core physics concepts. Compared to those who took the standard course, we found that the IPLS students were significantly more successful at building a model that combined ideas in a manner they had not previously seen, and at making complex decisions about how to apply an equation to a particular physical situation, although both groups displayed similar success at solving simpler problems. Both groups identified and applied simple models that they had previously used in very similar contexts, and executed straightforward calculations, at statistically indistinguishable rates. We report both our findings and the rationale behind the development of the task, in the hopes that others may find this task either a valuable tool or a starting point to develop other such tasks. Further study is needed to determine the basis for the IPLS students’ stronger performance—namely, what aspects of the IPLS course support these students to be better prepared to do such modeling—as well as whether biological settings are important for IPLS students to succeed in flexible model building, and whether the ability to employ models flexibly persists over time.
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