Perceptions of Assessment Literacy among Current and Prospective Veterinary Students
Bibliographic record
Abstract
Developing assessment literacy is important for veterinary students because the demands of a veterinary medicine course require students to rapidly adapt to new ways of learning and assessment. In this study, we investigate the understanding of assessments at university from applicants and current veterinary students and how this understanding can be improved and developed throughout the course. Data were gathered from three groups-applicants, naïve veterinary students, and experienced veterinary students-using questionnaire-based surveys. Of the applicants, 69% expected university assessments to be different from those at school, whereas only 13% agreed they had a good idea of what assessments would be like at university. More than 50% of students in their first term agreed they had a good understanding of assessments at university, although students had no significant improvement in their understanding of assessments as they progressed through the course. All three groups agreed that having a better understanding of assessments would make them feel more confident about exams. We conclude that more could be done to prepare prospective veterinary students for different styles of assessments and that current veterinary students would benefit from the opportunity to develop their assessment literacy. An assessment literacy curriculum is therefore proposed to develop students' assessment literacy from high school through graduation. Further research could investigate the development of assessment literacy interventions aimed at both applicants and veterinary students.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".