Deep Dissection: Motivating Students beyond Rote Learning in Veterinary Anatomy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The profusion of descriptive, factual information in veterinary anatomy inevitably creates pressure on students to employ surface learning approaches and "rote learning." This phenomenon may contribute to negative perceptions of the relevance of anatomy as a discipline. Thus, encouraging deep learning outcomes will not only lead to greater satisfaction for both instructors and learners but may have the added effect of raising the profile of and respect for the discipline. Consideration of the literature reveals the broad scope of interventions required to motivate students to go beyond rote learning. While many of these are common to all disciplines (e.g., promoting active learning, making higher-order goals explicit, reducing content in favor of concepts, aligning assessment with outcomes), other factors are peculiar to anatomy, such as the benefits of incorporating clinical tidbits, "living anatomy," the anatomy museum, and dissection classes into a "learning context" that fosters deep approaches. Surprisingly, the 10 interventions discussed focus more on factors contributing to student perceptions of the course than on drastic changes to the anatomy course itself. This is because many traditional anatomy practices, such as dissection and museum-based classes, are eminently compatible with active, student-centered learning strategies and the adoption of deep learning approaches by veterinary students. Thus the key to encouraging, for example, dissection for deep learning ("deep dissection") lies more in student motivation, personal engagement, curriculum structure, and "learning context" than in the nature of the learning activity itself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it