Peer Assessment of a Final-Year Capstone Experience for Formative Evaluation of a Pathology Curriculum
Bibliographic record
Abstract
In spring of 2005, the authors implemented and evaluated a process at the Iowa State University College of Veterinary Medicine in which third-year students evaluated fourth-year students' performances on an advanced case-analysis assignment. This assignment, called the case correlation assignment, required a thorough integration and explanation of all ante- and post-mortem data for a specific hospital patient. Using a 21-point rubric, the necropsy course instructor and third-year students rated these assignments. Fourth-year students' performances on this assignment were used as an indicator of the success of the pathology curriculum. The authors evaluated the assessment process for feasibility, reliability, and validity. Many-facet Rasch analysis was used to determine item, case, and rater agreement. The assessment process produced good agreement among items and cases (VM4 student competence). Furthermore, most third-year students were able to reliably rate the case correlation assignments with no special training. The evaluation process was cost effective and occurred in the context of regular course assignments, thereby making it feasible. A case can be made that the overall process provides a valid measure of the pathology program's success in preparing students in the area of veterinary pathology.
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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 | 0.033 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".