Simulating a Computational Biological Model, Rather Than Reading, Elicits Changes in Brain Activity during Biological Reasoning

Clark, Caron A. C.; Helikar, Tomáš; Dauer, Joseph T.

doi:10.1187/cbe.19-11-0237

Cited by 7 publications

(3 citation statements)

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“…Representation format may impact how a student perceives a model, especially if presented in a modality that contrasts with their prior knowledge format. In past research, we have adapted the Goel and Stroulia (1996) Structure-Behavior-Function (S-B-F) framework when constructing biology models, where structures of a system are in boxes (nodes) and the behaviors/relationships among them are described on connecting arrows (links, edges), to illustrate how the system produces a function (Dauer et al, 2013;Long et al, 2014;Clark et al, 2020). The symbolic nature of SBF models places attention on the text within the boxes and on the labeled arrows (Figure 1).…”

Section: Research Aimsmentioning

confidence: 99%

Individual variation in undergraduate student metacognitive monitoring and error detection during biology model evaluation

Dauer,

Behrendt,

Elliott

et al. 2024

Front. Educ.

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IntroductionModels are a primary mode of science communication and preparing university students to evaluate models will allow students to better construct models and predict phenomena. Model evaluation relies on students’ subject-specific knowledge, perception of model characteristics, and confidence in their knowledge structures.MethodsFifty first-year college biology students evaluated models of concepts from varying biology subject areas with and without intentionally introduced errors. Students responded with ‘error’ or ‘no error’ and ‘confident’ or ‘not confident’ in their response.ResultsOverall, students accurately evaluated 65% of models and were confident in 67% of their responses. Students were more likely to respond accurately when models were drawn or schematic (as opposed to a box-and-arrow format), when models had no intentional errors, and when they expressed confidence. Subject area did not affect the accuracy of responses.DiscussionVariation in response patterns to specific models reflects variation in model evaluation abilities and suggests ways that pedagogy can support student metacognitive monitoring during model-based reasoning. Error detection is a necessary step towards modeling competence that will facilitate student evaluation of scientific models and support their transition from novice to expert scientists.

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Section: Research Aimsmentioning

confidence: 99%

Individual variation in undergraduate student metacognitive monitoring and error detection during biology model evaluation

Dauer,

Behrendt,

Elliott

et al. 2024

Front. Educ.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mathematical modeling and computational approaches help build the skills of computational and systems thinking, while simulators allow inquiry-driven science and hypothesis formation while reducing time required for experimentation [1, ? , 2].…”

Section: Introductionmentioning

confidence: 99%

“…The importance of improving the quantitative coverage of undergraduate biology education and interdisciplinary work is paramount [5, 4]. Illustrating quantitative concepts in an interdisciplinary way has been shown to complement the quantitative courses students are already taking, as well as facilitating the synthesis of complex concepts for students who may not be intrinsically motivated to learn quantitative techniques [5, 2, 6]. However, incorporating quantitative skills into existing courses remains challenging.…”

Section: Introductionmentioning

confidence: 99%

Integrating Math Modeling, Coding, and Biology in a CURE Lab

Dale

Craig

2022

Preprint

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The development of mathematical and quantitative skills is increasingly critical for biology students. Literacy in coding, statistics, and mathematical modeling enables students to engage in systems-thinking and the analysis of large data sets. Here we present a flexible tool illustrating concepts in mathematical modeling, coding, and biology for integration into both traditional 'cookbook' and inquiry-driven labs for freshmen biology students. We developed simple and complex mathematical models of potato catechol oxidase, a popular system to teach enzyme kinetics in undergraduate biology labs. We integrated both models into a freely-available web app for simulation and parameter estimation. The models are usable even if experimental details are unknown or poorly controlled, so that even novice students can work through the problem. We illustrate this by estimating the kinetic parameters of catechol oxidase in the complex model using data obtained from two sections of a course-based undergraduate research experience (CURE) freshman biology lab. Worksheets with questions motivating model building and simulation comprehension are provided. The effect of these exercises on students' opinions of math, biology, and coding are evaluated using pre- and post-test surveys and student feedback. Our results show that these tools illustrate enzyme kinetics mathematically, students are not intimidated by the degree of math or coding involved, and in some cases are interested to do more, despite being unaware of the focus of the lab when signing up.

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Use of decision‐support tools by students to link crop management practices with greenhouse gas emissions: A case study

Jabbour

McClelland

Schipanski

2021

Natural Sciences Education

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Global food systems contribute to greenhouse gas emissions, and the mitigation of these emissions is a critical component of addressing the challenge of climate change. A variety of decision-support tools are available for agricultural producers to use to estimate how their management decisions affect emissions. These tools, often free and online, can be incorporated into agriculture and natural sciences courses, providing an engaging and interactive way for students to learn about climate change mitigation in online courses. Here, we focus on three tools: COMET-Planner, COMET-Farm, and Cool Farm Tool. Each of these tools link agricultural management with estimated emissions but differ according to the scope of analysis and type of functionality. Our case study navigates how to best incorporate each tool into undergraduate courses -providing detailed examples focused on crop production. Teaching notes provide guidance on pairing these activities with lessons related to agricultural policy, science communication, and farm nutrient budgets. Instructors have considerable opportunity to incorporate agricultural decision-support tools into courses to support students connecting scientific concepts to real-world application.

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Simulating a Computational Biological Model, Rather Than Reading, Elicits Changes in Brain Activity during Biological Reasoning

Cited by 7 publications

References 63 publications

Individual variation in undergraduate student metacognitive monitoring and error detection during biology model evaluation

Individual variation in undergraduate student metacognitive monitoring and error detection during biology model evaluation

Integrating Math Modeling, Coding, and Biology in a CURE Lab

Use of decision‐support tools by students to link crop management practices with greenhouse gas emissions: A case study

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