Usefulness of Crossword Puzzles in Helping First-Year BVSc Students Learn Veterinary Terminology
Bibliographic record
Abstract
Appropriate terminology is essential for successful communication among health professionals. However, students have traditionally been encouraged to learn terminology by rote memorization and recall, strategies that students try to avoid. The use of crossword puzzles as a learning tool has been evaluated in other education disciplines, but not for terminology related to veterinary science. Hence, the objective of this study was to test whether crossword puzzles might be an effective aid to learning veterinary terminology. Forty-two first-year students enrolled in a Bachelor of Veterinary Science program were randomly divided into two groups and their previous knowledge of veterinary terms tested. One group received a list of 30 terms with their definitions. The other group received the same list plus six specially designed puzzles incorporating these 30 terms. After 50 minutes, both groups completed a post-intervention test and the results were compared statistically. The results showed that the students using the crossword puzzles performed better in the post-intervention test, correctly retaining more terms than the students using only rote learning. In addition, qualitative data, gathered through an electronic survey and focus group discussions, revealed a positive attitude among students toward the use of crossword puzzles.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".