Comparing the Effects on Learning Outcomes of Tablet-Based and Virtual Reality–Based Serious Gaming Modules for Basic Life Support Training: Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Serious gaming is recognized as a training tool due its potential for a risk-free educational environment. There is still limited research about using serious gaming modules for emergency skills training. OBJECTIVE: The aim of this study is to compare the effects on the knowledge level of participants after using a tablet-based serious game and a virtual reality (VR)-based serious game for Basic Life Support using a pretest/posttest method. METHODS: The study was designed as a randomized trial comparing pretest and posttest results. A tablet-based and VR-based serious game with identical content was used for 40 participants. Over half of them (22/40, 55%) were included in the VR group and just under half (18/40, 45%) were in the tablet group. Student t test and Wilcoxon signed rank tests were used to determine the relation between the dependent and independent variables. In order to determine the effect size of the results, the effect size calculator (Cohen d) for t test was used. There is a significant difference between pre- and posttest results in both groups (P=.001; Wilcoxon). RESULTS: Mean posttest results were significantly higher in both groups. The posttest results were significantly higher in the VR group in terms of pre- and posttest changes (P=.021; Student t test). CONCLUSIONS: Past research studies have shown that serious gaming presents a favorable additional tool for medical education. The results indicate that both serious gaming modules are effective and that VR-based serious gaming is more efficient in terms of learning outcome than tablet-based gaming.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".