Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/6808
Title: Using Causal Networks to Examine Resource Productivity and Coordination in Learning Science
Authors: Kuo, Eric
Weinlader, Nolan
Rottman, Benjamin
Nokes-Malach, Timothy
Keywords: Learning and Identity
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Kuo, E., Weinlader, N., Rottman, B., & Nokes-Malach, T. (2020). Using Causal Networks to Examine Resource Productivity and Coordination in Learning Science. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 875-876). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: We propose that causal networks representing canonical scientific models can be a useful analytic tool for specifying how student knowledge resources are aligned with canonical science as well as the ways that they need to be recoordinated in learning science. Using causal networks to analyze student-generated science explanations, we highlight three results that illustrate the ways in which student thinking can simultaneously align with and break from correct scientific reasoning. This initial study highlights the potential benefits of causal networks for specifying the role of student resources in learning science.
URI: https://doi.dx.org/10.22318/icls2020.875
https://repository.isls.org//handle/1/6808
Appears in Collections:ICLS 2020

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