Undergraduate Quantitative Biology Impact on Biology Preservice Teachers
Quantitative biology is a rapidly advancing field in the biological sciences, particularly given the rise of large datasets and computer processing capabilities that have continually expanded over the past 50 years. Thus, the question arises, How should K-12 biology teachers incorporate quantitative biology skills into their biology curriculum? The teaching of quantitative biology has not been readily integrated into undergraduate biology curricula that impact preservice teachers. This has potential to cascade effects downward into the quality of learning about quantitative biology that can be expected in K-12 contexts. In this paper, we present the perspectives of a mathematics educator, a science educator, and two biologists, and discuss how we have personally incorporated aspects of quantitative reasoning into our courses. We identify some common challenges relevant to expanding implementation of quantitative reasoning in undergraduate biology courses in order to serve the needs of preservice teachers-both in their disciplinary courses and methods courses. For example, time constraints, math pedagogical content knowledge, and personal views about the relevance of quantitative principles in biology teaching and learning can impact how and to what extent they become implemented in curricula. In addition, although national standards at the K-12 level do address quantitative reasoning, the emphasis and guidance provided are sparser than for other content standards. We predict that both K-12 standards and guidelines for undergraduate education will only increase in their emphasis on quantitative skills as computation, "big data," and statistical modeling are increasingly becoming requisite skills for biologists.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:82 |
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| Enthalten in: |
Bulletin of mathematical biology - 82(2020), 6 vom: 19. Mai, Seite 63 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mayes, Robert [VerfasserIn] |
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| Links: |
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| Themen: |
Biology |
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| Anmerkungen: |
Date Completed 16.08.2021 Date Revised 16.08.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11538-020-00740-z |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Quantitative biology is a rapidly advancing field in the biological sciences, particularly given the rise of large datasets and computer processing capabilities that have continually expanded over the past 50 years. Thus, the question arises, How should K-12 biology teachers incorporate quantitative biology skills into their biology curriculum? The teaching of quantitative biology has not been readily integrated into undergraduate biology curricula that impact preservice teachers. This has potential to cascade effects downward into the quality of learning about quantitative biology that can be expected in K-12 contexts. In this paper, we present the perspectives of a mathematics educator, a science educator, and two biologists, and discuss how we have personally incorporated aspects of quantitative reasoning into our courses. We identify some common challenges relevant to expanding implementation of quantitative reasoning in undergraduate biology courses in order to serve the needs of preservice teachers-both in their disciplinary courses and methods courses. For example, time constraints, math pedagogical content knowledge, and personal views about the relevance of quantitative principles in biology teaching and learning can impact how and to what extent they become implemented in curricula. In addition, although national standards at the K-12 level do address quantitative reasoning, the emphasis and guidance provided are sparser than for other content standards. We predict that both K-12 standards and guidelines for undergraduate education will only increase in their emphasis on quantitative skills as computation, "big data," and statistical modeling are increasingly becoming requisite skills for biologists | ||
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| 700 | 1 | |a Williamson, Brad |e verfasserin |4 aut | |
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