With Marcus Kubsch, Eric-Jan (E-J) Wagenmakers, and Mine Dogucu, I wrote an article that has been in the works for awhile - it’s Making sense of uncertainty in the science classroom: A Bayesian approach published in the journal Science & Education.
Coming to this topic and writing this paper were both journeys. Some papers come together quickly and easily—those that are at the end of a long chain of projects, collaborations, and papers. This one was different. This paper was in a new area for me (though I’d been working toward this since 2020 when I wrote a paper for the ISLS conference – and then collaborated with Marcus Kubsch on another ISLS conference paper on the topic in 2021).
Marcus was and is an incredible thought partner. We are both fairly squarely in science education, and we realized we needed greater expertise. We had talked with EJ Wagenmakers in the context of EJ’s amazing book, Bayesian Thinking for Toddlers, and so we messaged EJ to see whether he’d be up for working on a paper about how Bayesian thinking and even Bayesian statistical approaches could be useful for learners at the pre-college levels. Around this time, I heard Mine Dogucu on the Learning Bayesian Statistics podcast, and I reached out asking to see whether Mine would ever be interested in collaborating on data science education (especially Bayesian) research projects. We talked and realized we could benefit immensely from Mine’s expertise with this paper. I note that both EJ and Mine are both in the quadrant of the “expertise in Bayesian methods” and “expertise in teaching and learning” quadrant. Speaking for myself, I have much more expertise in the latter, but am a learner of the former, and so EJ and Mine made this paper technically solid while also accessible and I think useful to younger (than college-level) teachers and learners in science.
I’m so proud of the result. I’m also pleased that we were able to publish the paper open-access; it is here: https://link.springer.com/article/10.1007/s11191-022-00341-3
Here’s the 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.
As you can see, this is more of a conceptual/review paper, though we share practical strategies and a Shiny app we developed to make the kind of approach we advocate for concrete. If I am honest, this paper is much more of a starting than a stopping point: I have a lot more to learn, and I hope to spend the next five or mroe years making progress on some of the ideas we introduced here.