The Impact of the Images in Multiple-choice Questions on Anatomy Examination Scores of Nursing Students
Bibliographic record
Abstract
Visualizing effects of images on improved anatomical knowledge are evident in medical and allied health students, but this \nphenomenon has rarely been assessed in nursing students. To assess the visualizing effect of images on improving anatomical \nknowledge and to use images as one of the methods of gross anatomical knowledge assessment in nursing students, the \npresent study was repeated over two semesters. The results show that the percent class average (%) was significantly (P<0.006) \nincreased with the inclusion of more anatomical images in a multiple-choice anatomy exam compared to a similar exam with \nfewer images and was significantly (P<0.002) decreased by reducing the number of images by 50% compared to image-rich \nexams. However, examinations with an equal number of images did not alter the class average. The percent score of individual \nquestions from the examinations with images plus text was significantly (P<0.001) higher than the same questions with text only \nin both semesters. The findings of this study indicate that image inclusion in anatomy examinations can improve learning and \nknowledge, may help reduce cognitive load, recall anatomical knowledge, and provide a hint to an exam question.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".