Using causal networks to examine resource productivity and coordination in learning science

Eric Kuo, Nolan K. Weinlader, Benjamin M. Rottman, Timothy J. Nokes-Malach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication14th International Conference of the Learning Sciences
Subtitle of host publicationThe Interdisciplinarity of the Learning Sciences, ICLS 2020 - Conference Proceedings
EditorsMelissa Gresalfi, Ilana Seidel Horn
PublisherInternational Society of the Learning Sciences (ISLS)
Pages875-876
Number of pages2
ISBN (Electronic)9781732467262
StatePublished - 2020
Event14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020 - Nashville, United States
Duration: Jun 19 2020Jun 23 2020

Publication series

NameComputer-Supported Collaborative Learning Conference, CSCL
Volume2
ISSN (Print)1573-4552

Conference

Conference14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020
Country/TerritoryUnited States
CityNashville
Period6/19/206/23/20

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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