Healthcare Students' Experiences of Learner-Educator Cocreation of Virtual Simulations
Bibliographic record
Abstract
INTRODUCTION: Cocreating virtual simulations with learners during a course is an innovative approach to improving student preparation for real-world practice while helping simulationists meet learner needs, support authentic assessment, and maximize the impact of simulation-based learning. This study explores differences in healthcare students' experiences of learner-educator cocreation of virtual simulations (LECoVSs) using phenomenographic methods. Identifying differences in perceptions of LECoVSs enables educators to make evidence-informed decisions about engaging in simulation cocreation as a tool to maximize learning. METHODS: Phenomenography focuses on identifying different ways that participants can experience the same phenomenon, in this case, LECoVSs. The setting was a collaborative interprofessional simulation assignment between navigation and nursing students. Participants completed a demographic survey then submitted reflective journals completed during the course and/or an open-ended survey. Data analysis occurred in iterative stages, from familiarization with the data to grouping and interpreting themes. RESULTS: Nineteen open-ended surveys and 13 reflective journals from navigation and nursing students who completed the simulation assignment between 2021 and 2023 were analyzed. Students experienced LECoVSs in 4 increasingly complex ways: (1) supporting consistent student progress, (2) amending course expectations, (3) sharing decision-making, and (4) fostering mutual growth. CONCLUSIONS: Simulationists may leverage cocreation to improve student learning, access, empowerment, and professional growth. However, for students to achieve higher learning outcomes, educators need to clearly communicate the full potential of cocreation, how it can occur, and why it can support learning. This study's findings may be used as a framework for explaining simulation cocreation to students to maximize their learning.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".