The Edinburgh Electronic Veterinary Curriculum: An Online Program-Wide Learning and Support Environment for Veterinary Education
Bibliographic record
Abstract
The Edinburgh Electronic Veterinary Curriculum (EEVeC) is a purpose-built virtual learning support environment for the veterinary medicine program at the University of Edinburgh. It is Web based and adapted from a system developed for the human medical curriculum. It is built around a set of databases and learning objects and incorporates features such as course materials, personalized timetables, staff and student contact pages, a notice board, and discussion forums. The EEVeC also contains global or generic resources such as information on quality enhancement and research options. Many of these features contribute to the aim of building a learning community, but a challenge has been to introduce specific features that enhance student learning. One of these is a searchable lecture database in which learning activities such as quizzes and computer-aided learning exercises (CALs) can be embedded to supplement a synopsis of the lecture and address the key needs of integration and reinforcement of learning. Statistics of use indicate extensive student activity during evenings and weekends, with a pattern of increased usage over the years as more features become available and staff and students progressively engage with the system. An essential feature of EEVeC is its flexibility and the way in which it is evolving to meet the changing needs of the teaching program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.021 |
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".