Developing and Implementing an Assessment Method to Evaluate a Virtual Canine Anatomy Program
Bibliographic record
Abstract
A computer-based anatomy program, Virtual Canine Anatomy: The Head, was incorporated into a first-year veterinary dissection laboratory two years ago to address challenges inherent in the traditional pedagogical approach. The program uses specimen photographs, QuickTime Virtual Reality, and interactive features to help students study the dissection, osteology, and radiology of the canine head. Photographs of each phase of dissection are displayed in the program, along with dissection instructions. Students can click on anatomical structures in each photograph to highlight the selected structure and display a complete description of it. Related structures and views are accessible through hyperlinks. This study was designed to measure student and faculty attitudes toward the instructional software, to gauge its effect on student achievement, and to propose evaluation methodology and instrumentation for similar projects. Observations, interviews, focus groups, surveys, and test results were used for this assessment. Results suggest positive student and faculty attitudes toward the program. Students felt the program met their needs, increased their confidence and efficiency, and was easy to use. Both students and instructors felt the program was beneficial during dissection. There was no significant change in student achievement on course tests. Future research will measure the program's effect on student-instructor interactions.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".