Interactive Virtual Reality with Educational Feedback Loops to Train and Assess Veterinary Students on the Use of Anesthetic Machine
Bibliographic record
Abstract
The study objective was to assess acceptability, feasibility, likeability, and applicability of interactive virtual reality with feedback loops (VR) to teach and assess veterinary anesthesia machine operation. Data from 60 students were analyzed. Students learned and trained how to use the anesthesia machine components and connections and performed safety checks (such as the pressure check) using real and VR machines. Competency was assessed with oral/practical and VR exams. A questionnaire survey gathered student affective skill perception toward VR for education. Students perceived VR for veterinary education as positive, useful, likeable, and helpful to learn the anesthesia machine. VR appeared to increase cognitive load, inducing lower VR exam scores of 100 (92.4–97.9) when compared to oral/practical exams of 100 (98–99.8) with p = .018. Training times with either real or VR anesthesia machines were similar ( p = .71). A positive correlation was found between VR training times and VR exam scores (Spearman's correlation coefficient 0.5; p < .001). No correlations were identified between oral/practical exam scores and training times. Seventy two percent of the students ( n = 43) had never used VR before. Prior VR experience was not necessary to train using VR. Computer glitches and cybersickness are important drawbacks to consider when using VR for education. The study demonstrated that interactive, immersive VR received favorable reactions from students. The VR incorporated educational feedback loops can be utilized as a simulation trainer for veterinary education. However, inherent limitations should be considered.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".