More than Meets the Eye: An Interactive 3D Model of the Eye for Enhanced Learning of the Oculomotor System
Bibliographic record
Abstract
The functional anatomy of the oculomotor system is conceptually difficult for novice students. This is problematic given that the extraocular muscles represent a common site of clinical intervention in the treatment of ocular motility disorders and other diseased states. This study was designed to develop a novel, three‐dimensional (3D) model that is paired with an electronic interface, creating an interactive 3D learning module of the human oculomotor system. Development of the 3D model utilized the Visible Human Project (VHP) Female data set and was refined using multiple forms of 3D software. Undergraduate anatomy students will be exposed to the learning module in a randomized pseudo‐crossover design. Quantitative post‐experience testing of the 3D learning module will be compared to equivalent two‐dimensional (2D) learning materials. Qualitative Likert‐scale questionnaires will be used to assess subjective opinions towards the different learning resources. It is hypothesized that the 3D learning module will enhance students’ spatial and functional comprehension on post‐test measures as well as maintain a higher level of interest, when compared to equivalent traditional 2D educational materials. Grant Funding Source : Western Graduate Scholarship
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".