Examining changes in medical students’ emotion regulation in an online PBL session
Bibliographic record
Abstract
Given recent attention to emotion regulation (ER) as an important factor in personal well-being and effective social communication, there is a need for detection mechanisms that accurately capture ER and facilitate adaptive responding (Calvo & D’Mello, 2010). Current approaches to determining ER are mainly limited to self-report data such as questionnaires, inventories and interviews (e.g., Davis, Griffith, Thiel, & Connelly, 2015). Although beneficial, these self-report approaches have important shortcomings such as social desirability biases, recall issues, and inability to capture unconscious ER (Scherer, 2005). The research presented here explores this gap by examining the use of multimodal observational data as well as self-report data to more accurately capture ER. Specifically, this study develops and employs a multimodal analysis of emotion data channels (facial, vocal and postural emotion data channels) to provide a rich analysis of ER in an international case study of four medical students interacting in an emotionally challenging learning session (i.e., communicating bad news to patients) in a technology-rich learning environment. The findings reported in the paper can provide insights for educators in designing programs to enhance and evaluate ER strategies of students in order to regulate personal emotions as well as the emotional needs of others in stressful situations. This work also makes important contributions to the design of technology-rich environments to embed dynamic ER detection mechanisms that enable systems to gain a more holistic view of the participants, and to adapt instructions based on their affective needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".