Student Perceptions of the Introduction of Pig Production, Management, and Health Teaching into the Veterinary Curriculum of a Muslim-Majority Country: A Case Study in Jordan
Bibliographic record
Abstract
Previously, pig production was not covered in the Jordan University of Science and Technology’s (JUST) veterinary curriculum due to Jordan being a predominantly Muslim country, with few graduates practicing outside the Middle East. However, pig production, management, and health (PPMH) education is increasingly recognized as needed to meet the requirements to obtain European Association of Establishments for Veterinary Education (EAEVE) accreditation. This study assessed the introduction of pig-related teaching into JUST’s veterinary curriculum and student perceptions of this content. A teaching intervention consisted of lectures, group sessions, and a virtual reality (VR) tour of a pig production (PP) unit. To ascertain participants’ level of understanding of PPMH, perceptions of different teaching styles, and changes in perception and understanding of the topics, they answered a survey before and after the intervention. Students’ knowledge of PP, husbandry, and perceived importance of PP awareness increased significantly, with 90% ( n = 17) agreeing that the intervention improved comprehension of the areas mentioned. Participants’ interest in PPMH learning increased, with 75% ( n = 14) stating they would want to learn more about the topic. VR was significantly ranked the most useful in terms of learning ( p = .029), and all participants stated the VR tour had a positive impact on their learning experience. Based on the findings, the focus of pig-related teaching in settings with limited awareness because of sociocultural reasons should be directed toward student-led exercises, group work, and use of technology such as VR. As a result of this intervention, blended PP has now been introduced to the JUST veterinary curriculum.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".