Assessing the Influence of Gender, Learning Style, and Pre-entry Experience on Student Response to Delivery of a Novel Veterinary Curriculum
Bibliographic record
Abstract
We investigated whether a novel veterinary curriculum was biased toward a particular gender, learning style, or pre-university experience (entry following undergraduate degree or direct entry from secondary school). We found no significant difference (p>0.05) in overall performance of first-year male, female, graduate-entry, or school-entry students. Students rated live-animal practical classes and facilitated problem-based learning as the most favored method of teaching, and this response was not biased by gender or pre-vet school experience. Men rated multiple-choice question (MCQ) assessment more highly than women, but there was no significant difference (p>0.05) in male or female performance on MCQ examinations. Men and women also performed comparably well in essays (both knowledge based and critical), suggesting that the retention of knowledge and depth of understanding was not gender biased. However, men performed significantly (p<0.05) better on critical essays compared with knowledge-based essays, and this trend was shown for both graduate-entry and school-entry students alike. We found no significant difference (p>0.05) in performance between groups of students with multimodal, kinesthetic, or reading-writing learning styles. Students with an auditory preference consistently performed less well in all types of assessment (p<0.05), but the number of students in this group was very small. Students whose learning style could not be specifically determined by Visual, Auditory, Read/write, Kinesthetic (VARK) tests consistently performed better than other groups, but this finding was not significant. Our results indicate that the Nottingham veterinary course does not bias for or against any of the variables we investigated.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".