Assessing the Influence of Gender, Learning Style, and Pre-entry Experience on Student Response to Delivery of a Novel Veterinary Curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it