Student Motivation Using Virtual Reality in Human Anatomy and Physiology Courses
Bibliographic record
Abstract
This study investigates student motivation using virtual reality (VR) technologies in anatomy and physiology courses. Over a two-year period, 21 college students from nursing, paramedic, and biotechnology-health programs were recruited for this study. The participants were randomly assigned to either a group using immersive VR on Quest 2 headsets or a group using desktop-based VR on personal computers. Both groups utilized VR on the health education platform 3D-Organon. The study compares the intrinsic motivation between these two groups. Four subscales of the Intrinsic Motivation Inventory were employed for this study. The immersive VR group was statistically significantly higher on the interest/enjoyment and perceived competence subscales. There was no significant difference between the two groups on the pressure/tension and perceived choice subscales. This study demonstrates VR's potential in boosting student motivation in human anatomy and physiology courses. Due to limited participation in pre- and post-assessment tools, content-based learning gains could not be compared. This highlights challenges in conducting VR studies in postsecondary institutions, including volunteer bias, curriculum integration barriers, student recruitment, and survey fatigue. These insights are critical for administrators and pedagogical designers when evaluating wider VR adoption in health and science education.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".