A Comparison of Linear, Fixed-Form Computer-Based Testing versus Traditional Paper-and-Pencil-Format Testing in Veterinary Medical Education
Bibliographic record
Abstract
Computerized testing has made significant inroads into veterinary education. Traditional paper-and-pencil examination formats are being replaced by computer-based testing (CBT). Computer-administered, fixed-form tests, because they mimic most closely the familiar fixed-response paper-and-pencil test formats, might intuitively seem to be inherently equivalent to their paper-and-pencil counterparts. However, research examining test-mode effects on student performance presents a very mixed picture. Additionally, students often report that they feel their performance is adversely affected by CBT and that their grades on the computer-based exams are lower than they would have been on the more familiar paper-and-pencil format. In order to address student perceptions of negative impact and the mixed nature of the published research results on the topic, a study was conducted to assess whether the transition from paper-and-pencil to equivalent linear CBT exams did, in fact, affect students' examination scores. This study found no evidence for significant test-mode effects on student scores as a result of the introduction of computer-based testing into the veterinary curriculum.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".