Changes in Student Perceptions and Study Strategies Over Time in a Veterinary Clinical Pathology Course Using Case-Based Instruction
Bibliographic record
Abstract
Veterinary students are challenged to develop new, nonlinear ways of thinking as they learn diagnostic reasoning skills. To support this process, we use real-life cases in our clinical pathology course. Changes in student perceptions regarding the use of cases and changes in study strategies over time have not been previously investigated or compared to student grades. Students participated in three voluntary online surveys that included 4-point Likert scale questions and open-ended questions on the helpfulness of cases for learning and study strategies used during the course. We used Friedman tests to detect any differences in perceptions over time; McNemar's test and Wilcoxon signed-rank tests were used to detect any differences in study strategies over time. Fisher's exact tests were used to examine the association between the Likert scale responses and grades in quartiles. Before beginning the course, 29% of students responded that cases were very helpful to their learning, with similar responses for helpfulness in applying course material and grasping important concepts. There was a significant trend of increasing positivity over the duration of the course, with 74% responding that cases were very helpful at the end of the course. The most-reported study strategy was working individually on cases before the midterm (74% of students), and the most helpful study strategy was attending class regularly (88% reported it as very helpful). Study strategies did not change significantly over time. Overall, perceptions and study strategies did not vary significantly with grades.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".