Getting Started with Curriculum Mapping in a Veterinary Degree Program
Bibliographic record
Abstract
The Royal (Dick) School of Veterinary Studies at the University of Edinburgh, UK, recently initiated a curriculum-mapping project to develop a tool that would facilitate curriculum review, improve integration and clarity across the curriculum, and provide a transparent method of demonstrating outcomes for quality-assurance purposes. The key finding from this project was that the curriculum-mapping process is a more resource-intensive undertaking than expected, and one that should not been taken lightly. At the time the project began, no commercial software was available that could be integrated with the program's other online systems or had content appropriate to an outcomes-based veterinary degree program. We recommend that future projects ensure a minimum of one dedicated full-time staff member, plus adequate educational technology support to develop a coherent and consistent format for the curriculum map that is integrated with the rest of the local online environment. Identifying the main focus of the map is also recommended at an early stage, as is the instigation of a small-scale pilot exercise to identify major local issues before starting the full mapping process. Future sustainability and development of a curriculum map also require buy-in from colleagues to ensure that relevant components of the map (e.g., learning objectives) are maintained and developed appropriately. This article is aimed at our colleagues who are considering starting a curriculum-mapping process at their institutions; we provide a brief overview of curriculum mapping, based on current literature, and then illustrate the process using our own experiences.
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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 | 0.034 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".