Immersive and non-immersive virtual reality: A quasi-experimental study in undergraduate nursing education
Bibliographic record
Abstract
• Immersive and nonimmersive VR effectively fostered engagement and learning. • Compared to nonimmersive VR, immersive VR increased confidence and enthusiasm. • Non-immersive VR emerged as a viable alternative with similar educational benefits. Immersive virtual reality (VR) is considered more engaging and realistic than non-immersive VR, but direct comparisons in nursing education are limited. This non-randomized quasi-experimental study explored undergraduate nursing students’ experiences in a home care simulation experience using immersive VR at the university (via VR headsets) or non-immersive VR at home (desktop simulation). A post-test survey incorporating qualitative feedback assessed engagement, satisfaction, confidence in learning, cognitive load, mental effort, and clinical reasoning. Engagement levels were similar across VR modalities. Immersive VR participants reported higher confidence and enthusiasm, while non-immersive VR participants reported greater mental effort and intrinsic cognitive load. Satisfaction, extraneous cognitive load, essential cognitive load, and clinical reasoning showed no significant differences between groups. Both immersive and non-immersive VR supported student engagement and learning. Remote, non-immersive VR emerged as a cost-effective alternative that offers similar educational benefits while requiring fewer resources.
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.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".