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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 |
Files in This Item:
File | Size | Format | |
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CSCL2024_403-404.pdf | 301.08 kB | Adobe PDF | View/Open |
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