Quantitative and Qualitative Evaluation of a Drawing Game Designed to Develop Visual Observational Skills in Veterinary Students Learning Cytology
Bibliographic record
Abstract
Many areas of veterinary medicine demand accurate, unbiased observation, including diagnostic microscopy. Fine arts-based training and drawing exercises have been used to develop medical and veterinary students' visual observational skills and improve learning retention. In this study, we created a drawing game in which students took turns describing a microscopic image for their partner to draw, with the aim of developing visual and descriptive skills in veterinary students learning cytology. The study used a pre-/midpoint-/post-test design, with students completing two rounds of drawing, then swapping roles between "describer" and "drawer" for two more rounds. Tests were evaluated qualitatively using content analysis and scored using an expert rubric. Scores were compared between pre- and post-tests to evaluate the effect of the game on diagnostic accuracy, and between pre- and midpoint-tests of describers versus drawers to evaluate the effect of the role. Qualitative observations were recorded about the classroom environment and drawings. Students also completed a questionnaire about the experience that included Likert scale and free-text questions. There was no significant difference between pre- and post-tests or between roles, but questionnaire responses indicated that students enjoyed the game and found subjective benefit, including a perceived increase in understanding the importance of observational and descriptive skills. Student descriptions highlighted weaknesses in identifying cytoplasmic and nuclear features, which may indicate areas to target in cytology education. Overall, our results indicate that the drawing game provided qualitative benefits and promoted student engagement, and it could be adapted for use in other visual subjects, such as radiology or gross pathology.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".