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|Title:||Learning Across Space, Time, and Scale: A Bayesian Perspective|
|Authors:||Tutwiler, M. Shane|
Grotzer, Tina A.
|Publisher:||International Society of the Learning Sciences|
|Citation:||Tutwiler, M. S. & Grotzer, T. A. (2013). Learning Across Space, Time, and Scale: A Bayesian Perspective. In Rummel, N., Kapur, M., Nathan, M., & Puntambekar, S. (Eds.), To See the World and a Grain of Sand: Learning across Levels of Space, Time, and Scale: CSCL 2013 Conference Proceedings Volume 2 — Short Papers, Panels, Posters, Demos & Community Events (pp. 371-372). Madison, WI: International Society of the Learning Sciences.|
|Abstract:||Theories of causal inference and pattern recognition based on machine learning have been proposed as normative models of human learning. To date, these theories fail to include explanations for why humans are biased towards some types of data (such as surprising or confirming) over others. In this poster we will provide a novel explanation for this, and use this hybrid theory to highlight areas of prior CSCL research that successfully supported student learning across space, time, and scale, as well as propose future research.|
|Appears in Collections:||CSCL 2013|
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