Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/7485
Title: Coherence across Conceptual and Computational Representations of Students’ Scientific Models
Authors: Hutchins, Nicole M.
Basu, Satabdi
McElhaney, Kevin W.
Chiu, Jennifer L.
Fick, Sarah J.
Zhang, Ningyu
Biswas, Gautam
Keywords: Learning Sciences
Issue Date: Jun-2021
Publisher: International Society of the Learning Sciences
Citation: Hutchins, N. M., Basu, S., McElhaney, K. W., Chiu, J. L., Fick, S. J., Zhang, N., & Biswas, G. (2021). Coherence across Conceptual and Computational Representations of Students’ Scientific Models. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 330-337). Bochum, Germany: International Society of the Learning Sciences.
Abstract: We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT.
URI: https://doi.dx.org/10.22318/icls2021.330
https://repository.isls.org//handle/1/7485
Appears in Collections:ISLS Annual Meeting 2021

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