The Effect of Repeated Review of Course Content on Medium- and Long-Term Retention in an Elective Veterinary Cardiology Course
Bibliographic record
Abstract
Assessment in veterinary medicine is challenging given the high volume of material and high cognitive load. Differing opinions exist regarding the utility of comprehensive final examinations in veterinary courses. A mixed-methods prospective randomized trial was undertaken with veterinary students ( n = 47) in an elective cardiology course. All students received identical content, which was presented through weekly in-class lectures (eight total lessons). They were given access to four different formats of study aids and completed quizzes of content knowledge within 1 week of each lesson. For the cumulative final examination (post-test), students were explicitly instructed to study only for four out of the eight lessons. Students then completed a delayed post-test 3–6 months after completion of the course. Quantitative and qualitative data were collected in the form of in-depth pre- and post-course surveys. Overall, studying a particular lesson for the post-test resulted in higher scores for six out of eight lessons in the post-test ( p < .05) but did not result in significantly higher scores for any lesson in the delayed post-test. Time spent studying for individual quizzes, as well as other measures of study habits, decreased significantly throughout the semester. Among study aids, student preference was highest for instructor-provided graphic organizers, and this preference persisted across the semester with a shift away from instructor-provided outlines over time ( p = .043). Free-text survey responses showed that students had realistic expectations for the impact of studying for the post-test and appreciated exposure to a variety of study aid formats. In an elective veterinary cardiology course, review of material prior to a cumulative final examination led to improved short-term performance but no difference in retention 3–6 months later.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".