A practical guide to virtual debriefings: communities of inquiry perspective
Bibliographic record
Abstract
Many simulation programs have recently shifted towards providing remote simulations with virtual debriefings. Virtual debriefings involve educators facilitating conversations through web-based videoconferencing platforms. Facilitating debriefings through a computer interface introduces a unique set of challenges. Educators require practical guidance to support meaningful virtual learning in the transition from in-person to virtual debriefings. The communities of inquiry conceptual framework offer a useful structure to organize practical guidance for conducting virtual debriefings. The communities of inquiry framework describe the three key elements-social presence, teaching presence, and cognitive presence-all of which contribute to the overall learning experience. In this paper, we (1) define the CoI framework and describe its three core elements, (2) highlight how virtual debriefings align with CoI, (3) anticipate barriers to effective virtual debriefings, and (4) share practical strategies to overcome these hurdles.
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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 | 0.037 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".