Facilitators’ experiences with virtual simulation and their impact on learning
Bibliographic record
Abstract
BACKGROUND: Increasingly, virtual simulations are being integrated into higher education. A successful experience goes far beyond simply offering learners access to a virtual simulation; it requires a facilitator who understands the learners' needs and course objectives, choses the right virtual simulation for the learner, creates a welcoming space that promotes learning, and evaluates the experience. METHODS: Facilitators from three different healthcare programs and six educational institutions and students from two different healthcare programs were included in this exploratory qualitative research study. Interviews and focus groups and thematic analysis were conducted to understand the role of the facilitator when using virtual simulations and their impact on student learning. RESULTS: The facilitator themes were supported by the student focus groups. The first theme, the facilitator experience, included sub-themes of simulation pedagogy and debriefing with a practice partner. The second theme was virtual simulation: impact on learning and included sub-themes on student outcomes, technology and design, and repetitive play. CONCLUSION: Effective facilitation skills are integral to quality virtual simulation experiences. Trained facilitators help students achieve virtual simulation learning outcomes and prepare for clinical practice.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".