I’m participating in the 2020 International Conference of the Learning Sciences (ICLS) Early Career Workshop.
It has been great. Kudos to the organizers for still carrying out the workshop virtually.
Part is presenting on an in-progress project; I’m presenting on a project that is at a very early stage - trying to explore what role Bayesian methods may have for K-2 science teaching and learning. I prepared a short presentation (below).
To try to summarize the argument:
- Kids intuitively make sense of the world in a Bayesian way; hardly anyone thinks about the world in terms of null hypothesis statistical testing, which can have a counter-intuitive logic and produces results which can be challenging to properly interpret
- Learners bring with them ideas about the world which can serve as resources for their learning; these ideas can be used within a Bayesian framework for making sense of the world as their “priors” about what they think is happening
- Science teachers care most about their students’ developing an understanding of core science ideas and practices, instead of formal statistical models, but Bayesian methods emphasize making statements about quantities or relationships of interest—rather than test statistics (e.g., t and F) and p-values.
The paper this presentation is based on is available here: https://edarxiv.org/7rptw/