Patterns of SRL and SSRL behaviors among medical trainees predict electrodermal activity at distinct stages of high-fidelity simulation training¶

Claim · All claims
Evidence · 2 studies · 1 review r2, 1 associational r1 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? Different patterns of SRL and SSRL behaviors were predictive of EDA at distinct stages of the simulation, indicating that learners' regulatory needs shift over time. → Joni Lämsä 2026

q2 i? Supervised machine learning models using SRL and SSRL codes, behaviors, and self-report data predict trainees' EDA, with predictive relationships varying by simulation segment. → Moreno 2025

Evidence¶

Joni Lämsä 2026¶

Joni Lämsä, Susanne de Mooij, Martine Baars, Inge Molenaar, Sanna Järvelä. (2026). Self-Regulated Learning, Multimodal Data, and Analysis Grid: Where Are We Now and Where Are We Going? Educational Psychology Review. https://doi.org/10.1007/s10648-025-10113-4

q2 · i? · review · r2

The commentary summarizes Moreno et al.'s (2025) empirical study in which "Sixty-two medical residents participated in simulations coded for behavioral and regulatory strategies, with EDA data collected via wearable sensors." Supervised machine learning (Lasso regression) predicted average EDA peaks per segment across the three phases of the simulation task.

"Results showed that different patterns of SRL and SSRL behaviors were predictive of EDA at distinct stages of the simulation, highlighting how learners' regulatory needs shift over time."

Moreno 2025¶

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

q2 · i? · associational · r1

Cross-sectional multimodal study of 62 medical residents in 19 teams completing one high-fidelity simulation each; Lasso regression models predicted EDA from video-coded SRL/SSRL strategies, behaviors, and self-report. The article reports that "varying SRL and SSRL codes can predict EDA" across segments.

"Results indicated that varying SRL and SSRL codes can predict EDA based on the regulatory needs of learners at different segments of the simulation."

Discussion¶

Related Claims¶

• Mind wandering can both prompt SRL and function as a regulatory strategy, and multimodal machine-learning sensing offers scalable assessment of attentive states — related
• SSRL 'Facilitation' codes differ between the 'Beginning' and both the 'Escalation' and 'Peak' segments of medical simulations — related
• SRL 'Execution' strategy codes differ between the 'Beginning' and both the 'Escalation' and 'Peak' segments of medical simulations — related
• SRL 'Evaluation' strategy codes differ between the 'Beginning' and 'Peak' segments of medical simulations — related
• In case examples, leaders and team members showed markedly different average peaks-per-segment EDA values during the same simulation segments — related
• Two distinct group deliberation patterns emerge in response to regulation triggers: the Plan and Implementation Approach (PIA) and the Trials and Failure Approach (TFA) — related
• Most groups maintain stable deliberation patterns across triggering events and fail to adapt to emerging regulatory needs — related
• In the beginning-segment Lasso model, participant role accounted for 15% of the final prediction model — a narrower finding that bears on this claim
• In medical simulation training, socially shared regulation relates to subsequent emotional stabilization, with electrodermal activity modeled as a dependent variable — possibly the same claim (merge candidate)
• Lecture-based learning events show a clearer decline in EDA across within-event segments than flipped learning events — related
• Electrodermal activity during learning events shows no robust association with post-event emotional valence — related