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dc.contributor.authorLee, Hee-Sun
dc.contributor.authorPallant, Amy
dc.contributor.authorPryputniewicz, Sarah
dc.contributor.authorLord, Trudi
dc.identifier.citationLee, H., Pallant, A., Pryputniewicz, S., & Lord, T. (2017). Articulating Uncertainty Attribution as Part of Critical Epistemic Practice of Scientific Argumentation In Smith, B. K., Borge, M., Mercier, E., and Lim, K. Y. (Eds.). (2017). Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017, Volume 1. Philadelphia, PA: International Society of the Learning Sciences.en_US
dc.description.abstractModels are important in discovering trends, developing and testing theories, and making predictions about complex systems. Since models cannot represent all known and unknown aspects of how nature operates, claims based on model-based data inevitably contain uncertainty. This study explores (1) how high school students attribute sources of uncertainty when prompted as part of an argumentation task and (2) how intelligent feedback may guide them to become more cognizant about deep uncertainty associated with model-based data. Phenomenological analyses of students’ uncertainty attributions (N = 840) identified five distinct patterns: self-introspection, personal theories and experiences, data source acknowledgement, scientific description based on singular causal accounts and frequency of observations, and deep uncertainty based on epistemic or ontic accounts. Discourse captured on video illustrated how intelligent feedback enhanced uncertainty attribution.en_US
dc.publisherPhiladelphia, PA: International Society of the Learning Sciences.en_US
dc.titleArticulating Uncertainty Attribution as Part of Critical Epistemic Practice of Scientific Argumentationen_US
dc.typeBook chapteren_US
Appears in Collections:CSCL 2017

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