Multiple-Choice Questions in Small Animal Medicine: An Analysis of Cognitive Level and Structural Reliability, and the Impact of these Characteristics on Student Performance
Bibliographic record
Abstract
Students entering the final year of the veterinary curriculum need to integrate information and problem solve. Assessments used to document competency prior to entry to the clinical environment should ideally provide a reliable measurement of these essential skills. In this study, five internal medicine specialists evaluated the cognitive grade (CG) and structural integrity of 100 multiple-choice questions (MCQs) used to assess learning by third-year students at a United States (US) veterinary school. Questions in CG 1 tested factual recall and simple understanding; those in CG 2 required interpretation and analysis; CG 3 MCQs tested problem solving. The majority (53%) of questions could be answered correctly using only recall or simple understanding (CG 1); 12% of MCQs required problem solving (CG 3). Less than half of the questions (43%) were structurally sound. Overall student performance for the 3 CGs differed significantly (92% for CG 1 vs. 84% for CG 3; p = .03. Structural integrity did not appear to impact overall performance, with a median pass rate of 90% for flawless questions versus 86% for those with poor structural integrity ( p = .314). There was a moderate positive correlation between individual student outcomes for flawless CG 1 versus CG 3 questions ( rs = 0.471; p = < .001), although 13% of students failed to achieve an aggregate passing score (65%) on the CG 3 questions. These findings suggest that MCQ-based assessments may not adequately evaluate intended learning outcomes and that instructors may benefit from guidance and training for this issue.
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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 | 0.013 | 0.056 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".