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Medical simulations allow trainees to work within teams to develop their self-regulated learning (SRL) and socially-shared regulated learning (SSRL) skills (Bransen et al., 2022). Both skillsets help to better prepare medical trainees for the multifaceted challenges inherent in clinical practice. SRL skills are imperative in empowering learners to optimize their performance and become autonomous guiders of their own learning (Jarvela & Hadwin, 2013), while SSRL skills are needed to ensure that teams can work collectively to regulate their behaviors and to regulate their own learning to make decisions (Hadwin & Oshige, 2011). Questions remain about not only how medical trainees' behaviors, regulation strategies, and physiological responses vary while they participate in a high-fidelity medical simulation, but how additional data channels to measure human response can provide indicators of teams' regulation strategies. Using a mixed-methods convergence design incorporating multimodal data (Azevedo & Gašević, 2019), including behavioral, SRL and SSRL codes, and electrodermal activity, researchers studied twenty-nine (N = 29) 1st to 3rd year medical residents as they engaged in high-fidelity simulation scenarios. Results suggest that the mean-level of psychophysiological activation increase as simulations progress, in conjunction with an increase in team-regulated learning strategies to manage the effective provision of patient care from initial contact through to the delivery of critical procedures. These results provide valuable insights into the advancement of a team regulation-based framework within a high-fidelity medical simulation environment, leveraging multimodal data to reach an understanding of medical trainees' adoption of team-based approaches to team-regulation during simulation scenarios. [ABSTRACT FROM AUTHOR]

Research on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co- and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system. Learner-PA interactions were examined across two scaffolding conditions: prompt and feedback (PF), and control. Learners’ compliance to follow the PA's prompts and feedback in the PF condition were also examined. Results demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time. Descriptive statistics revealed that when subgoals were set collaboratively between learners and the PA, they generally lead to higher proportional learning gains when compared to less collaboratively set goals. Taken together, the results provide preliminary evidence that learners are both willing to engage in and benefit from collaborative interactions with PAs when immediate, directional feedback and the opportunity to try again are provided. Implications and future directions for extending co- and socially-shared regulated learning theories to include learner-PA interactions are proposed. [ABSTRACT FROM PUBLISHER]