Virtual Dissection: An Interactive Anatomy Learning Tool
Bibliographic record
Abstract
The novelty of three-dimensional visualization technology (3DVT), such as virtual reality (VR), has captured the interest of many educational institutions. This study's objectives were to (1) assess how VR and physical models impact anatomy learning, (2) determine the effect of visuospatial ability on anatomy learning from VR and physical models, and (3) evaluate the impact of a VR familiarization phase on learning. This within-subjects, crossover study recruited 78 undergraduate students who studied anatomical structures at both physical and VR models and were tested on their knowledge immediately and 48 hours after learning. There were no significant differences in test scores between the two modalities on both testing days. After grouping participants on visuospatial ability, low visuospatial ability learners performed significantly worse on anatomy knowledge tests compared to their high visuospatial ability counterparts when learning from VR immediately (P = 0.001, d = 1.515) and over the long-term (P = 0.003, d = 1.279). In contrast, both low and high visuospatial ability groups performed similarly well when learning from the physical model and tested immediately after learning (P = 0.067) and over the long-term (P = 0.107). These results differ from current literature which indicates that learners with low visuospatial ability are aided by 3DVT. Familiarizing participants with VR before the learning phase had no impact on learning (P = 0.967). This study demonstrated that VR may be detrimental to low visuospatial ability students, whereas physical models may allow all students, regardless of their visuospatial abilities, to learn similarly well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".