Curricular Review and Renewal at Massey University: A Process to Implement Improved Learning Practices
Bibliographic record
Abstract
Curriculum managers of the Bachelor of Veterinary Science program at Massey University have undertaken major curricular review every 5-10 years and also made adjustments to the program as a result of student and other stakeholder feedback. New curricula introduced in 2003 and 2013 aimed to address specific stakeholder requirements in the veterinary, agricultural, and allied industries. The new curricula initially sought to strengthen clinical skills but more recently focused on the core professional skill of client communication, the integration of knowledge and clinical skills, and a better understanding of the effects of herd health interventions on farm economics. The need for greater emphasis on the veterinarian's role in One Health at the intersection of humans, animals, and the environment was also recognized. The most recent curricular review was preceded by faculty enlightenment and discussion about innovative models of medical education with a focus on student-centered and integrated learning. A new curriculum was introduced from 2013 that presented more material in its clinical context, attempted to manage curriculum overload through a focus on Day One Competences, implemented vertical and horizontal integration of subjects, and introduced more problem-based and student-centered learning. Regular reviews of student workload were needed to ensure that the objectives were achieved, but student feedback has generally been positive.
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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 | 0.164 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".