Enhancing Veterinary Education in Cambodia: Evaluation of Web-Based Resources in Teaching Herd Health and Epidemiology
Bibliographic record
Abstract
It can be challenging for veterinary schools in low- and middle-income countries (LMICs) to teach the 11 competencies identified by the World Organisation for Animal Health (WOAH) due to inadequate faculty and teaching resources. This article discusses the evaluation of web-based educational resources to support teaching in the Veterinary Faculty at the Royal University of Agriculture in Cambodia. Content- and pedagogy-based materials addressing herd health and epidemiology/disease investigation, their most urgent needs, were developed via a collaboration between Iowa State University, Ohio State University, and Massey University (New Zealand). Content-based resources were developed as a Moodle-based, server-mounted series of PowerPoint presentations, supported by a wide range of learning and assessment activities that the faculty could draw on in their teaching. Pedagogical resources were directed at strategic alignment between intended learning outcomes, teaching methods, and assessment. The use of these resources at the Royal University of Agriculture was evaluated by questionnaires, focus group discussions, and classroom observations. Results showed that the resources had been well received by the faculty, who drew on them to augment their own (Khmer-language) teaching materials, and to maintain teaching quality, especially during COVID-19 lockdowns. To a lesser degree, the faculty used the pedagogical materials and made modest shifts toward student-centered methods, which were observed to promote student engagement in their learning. The general agreement among the faculty on the overall benefits gained supports the development of future digital content and pedagogical materials to address the remaining nine competencies.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".