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This study aimed to explore the effect on group dynamics of statements associated with deep learning approaches (DLA) and their contribution to cognitive collaboration and model development during group modeling of blood circulation. A group was selected for an in-depth analysis of collaborative group modeling. This group constructed a model in a similar fashion to a target model and demonstrated within-group dialogic interaction patterns. It was found that statements associated with DLA contributed to the collaborative group dynamics by providing cognitive scaffolding and enabling critical monitoring, which together facilitated model development and students’ participation and understanding. In the model generation phase, the skills demonstrated indicated the use of statements associated with DLA as one student focused on the principles of blood circulation, thereby providing scaffolding for the other students. These students then generated another sub-model. In the model elaboration phase, statements associated with DLA elements such as  (AQ-a) and  (AQ-b) provided students with metacognitive scaffolding and enabled them to show their deep cognitive participation. Moreover, statements associated with DLA elements such as  or  enabled the students to monitor others’ models or ideas critically, showing that active cognitive interaction was taking place within the group. These findings reveal that individual learning approaches will bring a synergistic effect to a group modeling process and can lead to practical educational insights for educators seeking to use lessons based on group modeling.

Exploring the Impact of Students’ Learning Approach on Collaborative Group Modeling of Blood Circulation