Analyzing Multimodal Data to Understand Medical Trainees' Regulation Strategies and Physiological Responses in High- Fidelity Medical Simulation Scenarios

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 & Gaševic, 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.