Comparing Learning Outcomes of Machine-Guided Virtual Reality–Based Training With Educator-Guided Training in a Metaverse Environment: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) modules are commonly used for health care training, such as adult advanced cardiac life support (ACLS), due to immersion and engagement. The metaverse differs from current VR serious gaming by enabling shared social connections, while current VR modules focus on computer-based content without social interaction. Educators in the metaverse can foster communication and collaboration during training sessions. OBJECTIVE: This study aimed to compare learning outcomes of VR-based, machine-guided training with educator-guided, VR-based training in the metaverse environment. METHODS: A total of 62 volunteered students from Acibadem Mehmet Ali Aydinlar University Vocational School for Anesthesiology were randomly divided into 2 groups of 31 participants each: one group received VR-based training with machine guidance (MG), and the other received VR-based training with educator guidance (EG) in the metaverse. The members of both groups undertook VR-based basic training for ACLS. Afterward, the MG group was trained with a VR-based advanced training module, which provides training with full MG, whereas the EG group attended the VR-based, educator-guided training in the metaverse. The primary outcome of the study was determined by the exam score of the VR-based training module. Descriptive statistics defined continuous variables such as VR exam scores and time spent on machine- or educator-guided training. The correlation between training time and VR exam scores was assessed with the Spearman rank correlation, and nonnormally distributed variables were compared using the Mann-Whitney U test. Statistical significance was set at P<.05, with analyses executed by MedCalc Statistical Software (version 12.7.7). RESULTS: Comparing the VR test scores between the MG and EG groups revealed no statistically significant difference. The VR test scores for the EG group had a median of 86 (range 11-100). In contrast, the MG group scores had a median of 66 (range 13-100; P=.08). Regarding the correlation between the duration of machine-guided or educator-guided training and VR-based exam scores, for the MG group, =0.569 and P=.005 were obtained. For the EG group, this correlation was found to be =0.298 and P=.10. While this correlation is statistically significant for the MG group, it is not significant for the EG group. The post hoc power analysis (80%), considering the correlation between the time spent on training and exam scores, supported this finding. CONCLUSIONS: The results of this study suggest that a well-designed, VR-based serious gaming module with MG could provide comparable learning outcomes to VR training in the metaverse with EG for adult ACLS training. Future research with a larger sample size could explore whether social interaction with educators in a metaverse environment offers added benefits for learners. TRIAL REGISTRATION: ClinicalTrials.gov NCT06288087; https://clinicaltrials.gov/study/NCT06288087.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".