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Engineering Design (ED) challenges are increasingly used as a context to learn science. Research shows that there is a need for strategies that facilitate learners to identify, apply, and reflect on ways scientific principles can inform creation and evaluation of ED solutions. We investigate the use of contrasting cases and argumentation scaffolds to facilitate use of evidence-based reasoning in a CAD supported ED tasks. Elementary education majors in a physics course analyzed solutions to an ED problem in two conditions: 1) identify similarities and differences, 2) evaluate and produce an argument for a “good” design solution. We found that the argumentation condition used scientific evidence-based reasoning significantly more frequently in their responses than the control. Results indicate that the contrasting cases with argumentation scaffolds shows promise in facilitating students’ use of evidence-based reasoning in their ED tasks.
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Making Sense of Uncertainty in the Science Classroom: A Bayesian Approach
Abstract Uncertainty is ubiquitous in science, but scientific knowledge is often represented to the public and in educational contexts as certain and immutable. This contrast can foster distrust when scientific knowledge develops in a way that people perceive as a reversals, as we have observed during the ongoing COVID-19 pandemic. Drawing on research in statistics, child development, and several studies in science education, we argue that a Bayesian approach can support science learners to make sense of uncertainty. We provide a brief primer on Bayes’ theorem and then describe three ways to make Bayesian reasoning practical in K-12 science education contexts. There are a) using principles informed by Bayes’ theorem that relate to the nature of knowing and knowledge, b) interacting with a web-based application (or widget—Confidence Updater) that makes the calculations needed to apply Bayes’ theorem more practical, and c) adopting strategies for supporting even young learners to engage in Bayesian reasoning. We conclude with directions for future research and sum up how viewing science and scientific knowledge from a Bayesian perspective can build trust in science.
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- Award ID(s):
- 1937700
- PAR ID:
- 10377491
- Date Published:
- Journal Name:
- Science & Education
- Volume:
- 31
- Issue:
- 5
- ISSN:
- 0926-7220
- Page Range / eLocation ID:
- 1239 to 1262
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11191-022-00341-3
