Scaffolded Active Learning: Nine Pedagogical Principles for Building a Modern Veterinary Curriculum
Bibliographic record
Abstract
Veterinary discipline experts unfamiliar with the broader educational literature can find the adoption of an evidence-based approach to curriculum development challenging. However, greater societal and professional demands for achieving and verifying Day One knowledge and skills, together with continued progress in information generation and technology, make it all the more important that the defined period for initial professional training be well used. This article presents and discusses nine pedagogical principles that have been used in modern curricular development in Australia, the United Kingdom, and the United States: (1) outcomes-based curriculum design; (2) valid and reliable assessments; (3) active learning; (4) integrated knowledge for action; (5) tightly controlled core curriculum; (6) "just-in-time" rather than "just-in-case" knowledge; (7) vertical integration, the spiral curriculum, and sequential skills development; (8) learning skills support; and (9) bridges from classroom to workplace. Crucial to effective educational progress is active learning that embraces the skills required by the modern professional, made possible by tight control of curricular content. In this information age, professionals' ability to source information on a "just-in-time" basis to support high quality reasoning and decision making is far more important than the memorization of large bodies of increasingly redundant information on a "just-in-case" basis. It is important that those with responsibility for veterinary curriculum design ensure that their programs fully equip the modern veterinary professional for confident entry into the variety of roles in which society needs their skills.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".