Segment simulations into beginning, escalation, and peak phases before analyzing regulation and physiological activation¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 3 studies (2 associational, 1 review), q2 · 0 of 3 report an effect size · 3 claims rest on one study

Description¶

The article argues that examining EDA and its relationship to regulation is challenging without segmenting the task into smaller subsections, because simulation scenarios evolve and shift over time and require trainees to adopt different regulation patterns. The team segmented each video by consulting simulation scripts and patient deterioration events, then negotiated phase boundaries jointly. Segmentation produced analyzable sub-units: the average simulation took approximately 9 minutes and 23 seconds, with the beginning lasting 1 minute 55 seconds, escalation 4 minutes 6 seconds, and peak 3 minutes 40 seconds.

Design Implications¶

Context¶

Requirements¶

• Access to simulation scripts and scripted patient-deterioration events to guide segmentation; joint rater negotiation of phase boundaries

Constraints¶

Target Learners¶

• medical trainees in team-based simulation curricula

Target Learning Goals¶

• understanding how regulation strategies relate to physiological activation across task phases

Claims¶

• Varying SRL and SSRL codes predict medical trainees' EDA differently across the beginning, escalation, and peak segments of medical simulations [+W]
• SSRL 'Facilitation' codes differ between the 'Beginning' and both the 'Escalation' and 'Peak' segments of medical simulations [+W]
• SRL 'Execution' strategy codes differ between the 'Beginning' and both the 'Escalation' and 'Peak' segments of medical simulations [+W]
• SRL 'Evaluation' strategy codes differ between the 'Beginning' and 'Peak' segments of medical simulations [+W]

Related Strategies¶

• Develop a reliable coding scheme as a distinct stage of an SLA meta-analysis

Examples¶

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Key Sources¶

• Moreno, M., Grewal, K., Cutumisu, M., & Harley, J.M. (2025). Employing Machine Learning to Predict Medical Trainees’ Psychophysiological Responses, Self- and Socially-Shared Regulated Learning Strategies While Completing Medical Simulations. Education Psychology Review, 37 (70), 1-29. https://doi.org/10.1007/s10648-025-10044-0