Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/10576
Title: Collaborative Diagnostic Reasoning in a CSCL environment: A Multi Study Structural Equation Model
Authors: Brandl, Laura
Stadler, Matthias
Richters, Constanze
Radkowitsch, Anika
Schmidmaier, Ralf
Fischer, Martin R.
Fischer, Frank
Keywords: CSCL
Issue Date: 2024
Publisher: International Society of the Learning Sciences
Citation: Brandl, L., Stadler, M., Richters, C., Radkowitsch, A., Schmidmaier, R., Fischer, M. R., & Fischer, F. (2024). Collaborative Diagnostic Reasoning in a CSCL environment: A Multi Study Structural Equation Model. In Clarke-Midura, J., Kollar, I., Gu, X., & D'Angelo, C. (Eds.), Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning - CSCL 2024 (pp. 403-404). International Society of the Learning Sciences.
Abstract: In Collaborative Diagnostic Reasoning (CDR), good diagnostic outcomes depend on high quality diagnostic activities influenced by social skills, content, and collaboration knowledge. Analyzing data from three studies on simulation-based learning (504 medical students) using a structural equation model, our results challenge the current CDR model. We suggest prioritizing collaboration knowledge over social skills, emphasize the reduced impact of content knowledge in simulations, and distinguish between information elicitation and sharing, with the latter being more transactive.
Description: Poster
URI: https://doi.org/10.22318/cscl2024.951812
https://repository.isls.org//handle/1/10576
Appears in Collections:ISLS Annual Meeting 2024

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