Novel Card Games for Learning Radiographic Image Quality and Urologic Imaging in Veterinary Medicine
Bibliographic record
Abstract
Second-year veterinary students are often challenged by concepts in veterinary radiology, including the fundamentals of image quality and generation of differential lists. Four card games were developed to provide veterinary students with a supplemental means of learning about radiographic image quality and differential diagnoses in urogenital imaging. Students played these games and completed assessments of their subject knowledge before and after playing. The hypothesis was that playing each game would improve students' understanding of the topic area. For each game, students who played the game performed better on the post-test than students who did not play that game (all p<.01). For three of the four games, students who played each respective game demonstrated significant improvement in scores between the pre-test and the post-test (p<.002). The majority of students expressed that the games were both helpful and enjoyable. Educationally focused games can help students learn classroom and laboratory material. However, game design is important, as the game using the most passive learning process also demonstrated the weakest results. In addition, based on participants' comments, the games were very useful in improving student engagement in the learning process. Thus, use of games in the classroom and laboratory setting seems to benefit the learning process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".