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Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation

Xiaoshan Huang, Tianlong Zhong, Haolun Wu, Yeyu Wang, Ethan Churchill, Xue Liu, David Williamson Shaffer

TL;DR

This work addresses how facial-recognition-derived emotions co-occur with socially shared regulation of learning (SSRL) during computer-supported medical diagnosis tasks. It introduces a transmodal Epistemic Network Analysis (T/ENA) framework to integrate facial expressions and discourse, comparing novice and expert learner groups. Key findings show experts link high-arousal emotions like surprise and anger with socio-cognitive SSRL, while novices exhibit links with happiness and sadness and less coherent SSRL patterns, suggesting different regulatory strategies. The study highlights the potential for adaptive, scaffolded medical simulations and provides methodological advances for multimodal learning analytics in CSCW contexts.

Abstract

Computer-supported simulation enables a practical alternative for medical training purposes. This study investigates the co-occurrence of facial-recognition-derived emotions and socially shared regulation of learning (SSRL) interactions in a medical simulation training context. Using transmodal analysis (TMA), we compare novice and expert learners' affective and cognitive engagement patterns during collaborative virtual diagnosis tasks. Results reveal that expert learners exhibit strong associations between socio-cognitive interactions and high-arousal emotions (surprise, anger), suggesting focused, effortful engagement. In contrast, novice learners demonstrate stronger links between socio-cognitive processes and happiness or sadness, with less coherent SSRL patterns, potentially indicating distraction or cognitive overload. Transmodal analysis of multimodal data (facial expressions and discourse) highlights distinct regulatory strategies between groups, offering methodological and practical insights for computer-supported cooperative work (CSCW) in medical education. Our findings underscore the role of emotion-regulation dynamics in collaborative expertise development and suggest the need for tailored scaffolding to support novice learners' socio-cognitive and affective engagement.

Linking Facial Recognition of Emotions and Socially Shared Regulation in Medical Simulation

TL;DR

This work addresses how facial-recognition-derived emotions co-occur with socially shared regulation of learning (SSRL) during computer-supported medical diagnosis tasks. It introduces a transmodal Epistemic Network Analysis (T/ENA) framework to integrate facial expressions and discourse, comparing novice and expert learner groups. Key findings show experts link high-arousal emotions like surprise and anger with socio-cognitive SSRL, while novices exhibit links with happiness and sadness and less coherent SSRL patterns, suggesting different regulatory strategies. The study highlights the potential for adaptive, scaffolded medical simulations and provides methodological advances for multimodal learning analytics in CSCW contexts.

Abstract

Computer-supported simulation enables a practical alternative for medical training purposes. This study investigates the co-occurrence of facial-recognition-derived emotions and socially shared regulation of learning (SSRL) interactions in a medical simulation training context. Using transmodal analysis (TMA), we compare novice and expert learners' affective and cognitive engagement patterns during collaborative virtual diagnosis tasks. Results reveal that expert learners exhibit strong associations between socio-cognitive interactions and high-arousal emotions (surprise, anger), suggesting focused, effortful engagement. In contrast, novice learners demonstrate stronger links between socio-cognitive processes and happiness or sadness, with less coherent SSRL patterns, potentially indicating distraction or cognitive overload. Transmodal analysis of multimodal data (facial expressions and discourse) highlights distinct regulatory strategies between groups, offering methodological and practical insights for computer-supported cooperative work (CSCW) in medical education. Our findings underscore the role of emotion-regulation dynamics in collaborative expertise development and suggest the need for tailored scaffolding to support novice learners' socio-cognitive and affective engagement.
Paper Structure (13 sections, 3 figures)

This paper contains 13 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Socially Shared Regulation in Computer-Based Learning Environments
  4. Facial Recognition in Medical Simulation
  5. Method
  6. Computer-Based Medical Simulation
  7. Participants and Multi-Modal Data Sources
  8. Transmodal Epistemic Network Analysis (T/ENA)
  9. Result
  10. RQ1a. Co-Occurrence of Facial Recognition and SSRL Interactions
  11. RQ1b. Co-Occurrence Differences between Novice and Expert Learners
  12. Discussion
  13. Limitation and Future Works

Figures (3)

  • Figure 1: BioWorld Interface and Data Retrieval Processes in the Virtual Medical Diagnostic Task
  • Figure 2: Co-Occurrences of Facial Recognition of Emotions and SSRL Interactions in the Expert and Novice Groups
  • Figure 3: The Subtracted Network of Two Levels of Expertise