Assessing the Effectiveness of 3D-Printed Testes and Ovary Biomodels in Veterinary Reproduction Education: Student-Centered Approach
Bibliographic record
Abstract
The use of biomodels is prevalent across multiple educational disciplines, with a particular emphasis on their utilization in teaching the anatomy of organs. These tools have not only enriched education, but have also provided an alternative to the ethical and cultural controversies, increased costs, and health and safety risks associated with the use of live animals and cadavers. However, while there is limited data on testes and ovary biomodels in the literature, no findings on their effectiveness in education have been reported. Understanding the morphology of testicular and ovarian tissues is vital for veterinarians. This study aimed to investigate the effectiveness of three-dimensional (3D) printed testes and ovary biomodels in veterinary reproduction education and students' perspective on them. To assess their educational effectiveness, biomodels created to align with specific learning objectives were evaluated against slaughterhouse materials. This comparison was carried out on a total of 94 students divided into two groups. A questionnaire containing 19 different judgments was administered to determine students' attitudes toward biomodels. Following the assessments, students reported that they perceived biomodels to be a more advantageous resource than the slaughterhouse materials for their practical training ([Formula: see text]: 3.12). In addition, they strongly ([Formula: see text]: 4.14) expressed their wish to use biomodels in other practical fields of veterinary medicine education. As a result, this study demonstrated for the first time that testes and ovary biomodels can be produced to cover learning objectives in veterinary medicine education. In addition, it was observed that veterinary students supported and demanded the use of these biomodels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".