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|Title:||Using Causal Networks to Examine Resource Productivity and Coordination in Learning Science|
|Keywords:||Learning and Identity|
|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.|
|Appears in Collections:||ICLS 2020|
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