三个研究为基础的定量推理模块入门有机生物学实验室

CourseSource Pub Date : 2022-01-01 DOI:10.24918/cs.2022.48
E. Crispo, K. Ilves
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摘要

我们为有机体生物学入门课程设计了三个实验室模块,重点是定量推理和数据分析技能。模块1使用配对统计设计检验小龙虾螯合的二态性。模块2使用回归模型检验绦虫钩结构异速生长。模块3使用双样本统计方法检验两组植物之间气孔密度的差异。对于所有三个模块,我们强调使用置信区间来得出关于假设的统计结论。需要了解动植物的基本生物学知识,包括节肢动物、扁形蠕虫和维管植物。关于二态异速生长和蒸腾的背景阅读为提出问题和假设提供了必要的基础。对于学生和教师来说,熟悉一些R是必要的,尽管可以使用Excel或其他统计软件包修改这些活动以进行分析。这些模块可以独立教授,也可以作为课程的一个单元一起教授。正如美国科学促进会(AAAS)文件《愿景与变革:行动呼吁》(Vision and Change: A Call to Action)所述,使用定量推理的能力是所有生物学学生必须培养的核心能力。这些模块解决了对定量推理教学的要求,并提供了在更高级的课程中建立学生统计曲目所需的关键工具的实际操作的积极介绍。
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Three Research-Based Quantitative Reasoning Modules for Introductory Organismal Biology Laboratories
We have designed three laboratory modules for an introductory organismal biology course with an emphasis on quantitative reasoning and data analysis skills. Module 1 tests for dimorphism in crayfish chelae using a paired statistical design. Module 2 tests for allometric growth of tapeworm hook structures using a regression model. Module 3 tests for differences in stomatal densities between two groups of plants using a two-sample statistical approach. For all three modules, we emphasize the use of confidence intervals to draw statistical conclusions about hypotheses. Knowledge about the basic biology of animals and plants is required, including arthropods, platyhelminths, and vascular plants. Background reading on dimorphism, allometry, and transpiration provides the necessary foundation to develop questions and hypotheses. Some familiarity with R is necessary for both students and instructors, although the activities can be modified for analysis with Excel or another statistical package. These modules can be taught independently or together as a unit within a course. As stated in the AAAS document, Vision and Change: A Call to Action , the ability to use quantitative reasoning is a core competency that must be developed by all biology students. These modules address the call for instruction in quantitative reasoning and provide a hands-on active introduction to key tools that will be required to build students’ statistical repertoire in more advanced courses.
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