Use of Educational Puzzles for Learning Concepts of Clinical Diagnostic Imaging in Veterinary Medicine
Bibliographic record
Abstract
Learning the concepts of clinical radiology, including lesion identification and formulation of differential diagnosis lists, can be challenging for veterinary students. A series of educational puzzles with an overarching narrative was developed to help students learn the fundamental concepts of urogenital, thoracic, and spine imaging. Third-year veterinary students had the opportunity to use as many of the puzzles as they wished as a part of their studies in a semester-long imaging course, and students completed surveys to indicate which puzzle sections they used and provide their opinions of the activities. Graded performance in the course was correlated with how many puzzle activities students used. A small but statistically significant correlation was found between the number of puzzle sections used and midterm exam score, final exam score, and overall course score. Although most students who used the puzzles as a part of their studies enjoyed the activities, there was a dramatic decrease in usage over the semester, from 74% of survey respondents using the initial topic to a low of 27% utilization of the sixth topic, followed by a small rebound to 37% for the eighth topic (the review for the final exam). Thus, while developing a puzzle series is achievable and beneficial to student learning, possibly because of improved student engagement through increased variety in learning opportunities, further steps are necessary to encourage continued student engagement throughout the semester.
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 | 0.003 | 0.034 |
| 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) | 0.001 | 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".