Development and Evaluation of an Online Computer-Aided Learning (CAL) Package to Promote Small-Animal Welfare
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: The aims of the study, conducted as a student research project in the 2004-5 session, were to develop an interactive, online computer-aided learning (CAL) package on the topic of small-animal husbandry; to validate the resource as a suitable lecture replacement for first-year veterinary students; and to raise awareness of current guidelines and legislation relating to small-animal housing among local catteries and kennels and in the wider community. METHODOLOGY: Quantitative feedback was collected from student and teaching staff using paper-based questionnaires. Qualitative feedback was gathered from open questionnaire responses and through focus-group discussions with students. Student examination marks were compared for 2004 and 2005, allowing a comparison of student performance before and after the replacement of the traditional lecture with the CAL package. Ethical approval for the study was granted by the faculty's Ethics Committee. RESULTS AND CONCLUSIONS: The CAL package on small-animal housing was well received by teaching staff and students; student performance in examinations improved after the introduction of the CAL program, suggesting that it provides a suitable alternative to didactic teaching. The creation and distribution of the CAL package on CD-ROM and its availability via the Internet are intended to contribute to small-animal welfare education in local catteries and kennels and further afield. The package sets a precedent for the development of more Internet-based, student-authored CAL packages in the future, providing additional resources for independent learning.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".