This study explored how students' learning approaches impacted group dynamics and model development during a collaborative modeling activity on blood circulation. The researchers analyzed one group in depth that constructed a model similar to the target model and demonstrated good dialog within the group. They found that statements associated with deep learning approaches contributed positively to the group dynamics by providing cognitive scaffolding and enabling critical monitoring, which supported model development and student participation and understanding. Deep learning approaches, like focusing on principles and asking questions, facilitated generation of sub-models and elaboration of the overall model.

Exploring The Impact of Students' Learning Approach On Collaborative Group Modeling of Blood Circulation PDF

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