The impact of an immersive virtual reality anatomy lab for informal science education
Bibliographic record
Abstract
The use of virtual reality (VR) in anatomy education enables interactive exploration of the human body and can overcome challenges in informal education settings, such as access to donated human remains and spatial understanding of 3D structures. This study explored how individual factors-such as spatial ability, visual imagery skills, and susceptibility to cybersickness-affected high school-aged (15-18 years old) learners' experiences and perceived effectiveness of a VR anatomy lab as a learning tool. Results showed that learners with higher visual imagery skills (visualizers) report a statistically greater sense of relevance and satisfaction when learning anatomy using VR. Those parameters are reported to be less statistically significant by those with stronger mental rotation skills (rotators) who perceive VR as less beneficial. Overall, learners find VR engaging and effective for learning, with positive correlations observed between the perceived quality of the VR experience reported by the learners and the learners' motivation to learn, attention, confidence, satisfaction, and potential positive impact on their education (p < 0.05). However, both cognitive load and symptoms of cybersickness, while rare in this study population, were negatively correlated with the sense of relevance and confidence (p < 0.05). Our findings support the use of VR as a complementary tool in informal science education, particularly for those with diverse learning needs. Results also indicate that exposure to the VR anatomy lab positively influences learners' perceptions of career opportunities in STEAM, particularly in technology-enhanced fields such as medical imaging and surgical simulation, inspiring interest in fields they had not previously considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".