The Effectiveness of a 3D Computerized Tutorial to Enhance Learning of the Canine Larynx and Hyoid Apparatus
Bibliographic record
Abstract
Teaching the anatomy of the canine larynx and hyoid apparatus is challenging because dissection disassembles and/or damages these structures, making it difficult to understand their three-dimensional (3D) anatomy and spatial interrelationships. This study assessed the effectiveness of an interactive, computerized 3D tutorial for teaching the anatomy of the canine larynx and hyoid apparatus using a randomized control design with students enrolled in the first-year professional program at Oregon State University College of Veterinary Medicine. All first-year students from 2 consecutive years were eligible. All students received the traditional methods of didactic teaching and dissection to learn the anatomy of the canine larynx and hyoid apparatus, after which they were divided into two statistically equal groups based on their cumulative anatomy test scores from the prior term. The tutorial group received an interactive, computerized tutorial developed by the investigators containing 3D images of the canine larynx and hyoid apparatus, while the control group received the same 3D images without the computerized tutorial. Both groups received the same post-learning assessment and survey. Sixty-three first-year students participated in the study, 28 in the tutorial group, and 35 in the control group. Post-learning assessment and survey scores were both significantly higher among students in the computerized tutorial group than those in the control group. This study demonstrates that a 3D computerized tutorial is more effective in teaching the anatomy of the canine hyoid apparatus and larynx than 3D images without a tutorial. Students likewise rated their learning experience higher when using the 3D computerized tutorial.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".