Development and Evaluation of a Virtual Barn Tour in Swine Medicine
Bibliographic record
Abstract
The shortage of livestock veterinarians is a pressing issue in Germany and worldwide. To address this problem, the University of Veterinary Medicine Hannover, Germany, developed a digital-teaching module for swine medicine. The module, designed for first- and third-semester students, aims to provide a comprehensive understanding of swine medicine and animal husbandry. It consists of five 360° virtual barn tours of different pig farms, interactive questions, and case studies. The study evaluates the module's effectiveness in increasing students’ knowledge and understanding of swine medicine. Results demonstrate that the module has a positive impact on students’ knowledge, with the average number of correct answers increasing from 8.6 (of 14) in the pretest to 13.2 in the posttest. Knowledge improvement was measured using identical multiple-choice questions before and after module completion. Participants experienced a significant knowledge gain, especially those with less prior experience in the pig sector benefiting more from the module than those with more experience. The experience level had a significant effect on response behavior ( p = 0.0267); participants with moderate experience (Level 2) chose the response category I don't know significantly less often than those with the highest experience level (Level 4; p = 0.035). The results show that such a digital-teaching module has a high acceptance level among students. The study's findings are relevant for developing teaching materials in veterinary medicine. The study also highlights the importance of incorporating digital-teaching methods into veterinary education, particularly in swine medicine.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".