Facilitators’ experiences with virtual simulation and their impact on learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it