Author:
Rosenberg Joshua M.,Kubsch Marcus,Wagenmakers Eric-Jan,Dogucu Mine
Abstract
AbstractUncertainty 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.
Funder
National Science Foundation
IPN – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik an der Universität Kiel
Publisher
Springer Science and Business Media LLC
Reference125 articles.
1. Abd-El-Khalick, F., Bell, R. L., & Lederman, N. G. (1998). The nature of science and instructional practice: Making the unnatural natural. Science Education, 82(4), 417–436.
2. Adams, E. (1965). The logic of conditionals. Inquiry, 8(1–4), 166–197.
3. Aguilar, L. A., Luna, F. V., Robledo-Sánchez, C., & Arroyo-Carrasco, M. L. (2014). The infinite square well potential and the evolution operator method for the purpose of overcoming misconceptions in quantum mechanics. European Journal of Physics, 35(2), 1–15.
4. Albert, J. (2002). Teaching introductory statistics from a Bayesian perspective. Proceedings of the Sixth International Conference on Teaching Statistics, 1–14.
5. Albert, J., & Hu, J. (2020). Bayesian computing in the undergraduate statistics curriculum. Journal of Statistics Education, 28(3), 236–247. https://doi.org/10.1080/10691898.2020.1847008
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献