Learning With Virtual Reality in Nursing Education: Qualitative Interview Study Among Nursing Students Using the Unified Theory of Acceptance and Use of Technology Model
Bibliographic record
Abstract
BACKGROUND: Digital games-based learning is a method of using digital games to impart knowledge. Virtual reality (VR) programs are a practical application of this method. Due to demographic changes, the nursing profession will become increasingly important. These VR applications can be of use in training nurses for future professional challenges they may encounter. The continuous development of VR applications enables trainees to encounter simulated real life effectively and to experience increasingly concrete situations. This can be of great importance in nursing education, since 3-dimensionality enables a better visualization of many fields of activity and can prevent potential future errors. In addition to this learning effect, VR applications also bring an element of fun to learning. OBJECTIVE: The aim of this qualitative research effort is to observe the degree of acceptance of VR applications by nursing students in Germany. Various factors, including social influences, performance expectations, and effort expectations, are taken into consideration. METHODS: With a qualitative cohort study, the acceptance of nursing students towards VR applications in anatomy teaching was determined. The 12 participants were first asked to fill out a quantitative questionnaire on their sociodemographic characteristics and the extent to which they valued and liked using technology. The participants were then allowed to test the VR application themselves and were finally asked about their experience in a qualitative interview. For the collection of data and the analysis of results, the unified theory of acceptance and use of technology was used in this study. RESULTS: Overall, the study shows that the interviewed persons rated the VR application quite positively. The greatest influence in this was the personal attitude towards technology; the higher this affinity is, the more useful the VR application appears. Social influences can also increase the participant's own acceptance if peers have a positive attitude towards such applications. The study shows that the trainees' motivation to learn was increased by using VR. We believe this is because each trainee could learn individually and the VR application was perceived as an enjoyable activity. Nevertheless, the cost factor of implementing VR applications in nursing training is currently still an obstacle, as not every institution has such financial capacities. CONCLUSIONS: The extent to which the use of VR applications in the training of nursing staff is justified depends on the degree of personal acceptance. The collected results give good practice-oriented insight into the attitude of trainees towards VR. Many of the interviewed persons saw benefits in the use of VR technologies. As VR applications are constantly developing, it is necessary to conduct further studies on VR applications in nursing education and to include other possible disciplines in which these applications can be helpful.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".