Evolution of a Course in Veterinary Clinical Pathology: The Application of Case-Based Writing Assignments to Focus on Skill Development and Facilitation of Learning
Bibliographic record
Abstract
RATIONALE FOR STUDY: To encourage application and critical thinking, case-based writing assignments and grading rubrics were developed for use in a second-year core clinical pathology course. Objectives were to describe how this teaching technique was adapted to a large-class setting and how student perceptions of the learning experience guided modifications of this teaching technique over two presentations of the course and plans for a third presentation. Our goal was to enhance learning by encouraging application of course material to clinical situations and thereby improve students' ability to organize and communicate information. Furthermore, we evaluated the influence of instructor feedback on the learning process. METHODS: With assistance from the University of Minnesota Center for Writing, assignments and grading rubrics were developed. Students completed course evaluation surveys designed to elicit feedback on the impact of the assignments. RESULTS: Increased learner confidence was reflected in larger self-reported increases in understanding of the material and ability to apply information and in increased feelings of preparedness for class and examinations. A large majority of students advocated the use of such assignments in the course in future years, and modifications to make grading and evaluation of assignments more efficient are underway. CONCLUSIONS: Investment of faculty and student time in case-based writing assignments in the veterinary clinical pathology curriculum appears to increase student engagement with material and learner confidence. Future studies should address the impact of this type of assignment more specifically on clinical reasoning and communication skills and on long-term retention of material.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".