Evaluation of Veterinary Medical Student Retention of Pre-clinical Concepts with Various Experiential Learning Methods
Bibliographic record
Abstract
Many veterinary medical colleges have undergone curricular changes that have moved away from traditional lecture-based teaching in favor of evidence-based, experiential methods of instruction. Such a curricular reinvention occurred in 2018 at Michigan State University’s College of Veterinary Medicine, with individual courses using numerous instructional and learning methods. In the present study, three courses were assessed, two of which used a method of experiential learning, and the other utilizing a traditional lecture approach. The purpose of this study was to determine if the method of instruction impacted exam grades, content retention, and student perspective. Methods of teaching and learning were quantified for each course using the Classroom Observation Protocol for Undergraduate STEM. Following completion of each course, participants ( n = 27) retook the same final examination and participated in a survey 5 weeks later so their perspective could be evaluated. Mean scores on the initial examinations in the experiential learning courses were significantly higher than the mean score of the traditional lecture course ( p = .01). However, mean retake examination scores were similar for all courses ( p = .76). Students reported more confidence with course materials and examinations in courses that incorporated active learning strategies. Although true retention is difficult to assess in veterinary medicine, evaluation of student perspectives suggests the use of experiential learning methods primarily or in combination with lecture-based material to support student learning of pre-clinical concepts. Future controlled studies are needed to evaluate veterinary students’ short- and long-term learning and retention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".