An Impact of Images on Multiple‐Choice Questions in Anatomy Examination Score in Nursing Students
Bibliographic record
Abstract
Anatomy examinations often have a key visual component with the inclusion of many images, and research shows that visualization can have a positive effect on learning (Vorstenbosch et al., 2013) while altering learner's cognitive load, memory and examination anxiety (Mayer RE, 2005). However, most of the studies on the effects of images on anatomy examinations are focused in medical, pre‐nursing, or pre‐health science students but have not yet been assessed in nursing students. To assess the effects of images on anatomy examination scores, quizzes and final examination containing multiple‐choice questions with greater or lesser anatomical images was assessed. Results show that inclusion of more anatomical images in a multiple‐choice anatomy quiz (quizzes #1 and 2, section BN01) significantly (P<0.01) increased their percent class average compared to a similar quiz with fewer images (Quiz #1 and 2, section BN02). In quiz#3, reducing the number of images by 50% in anatomy section BN01 resulted in a significant decrease (P<0.05) in their percent class average compared to an image‐rich quiz in section BN02. However, the final anatomy examination containing an equal number of images in both anatomy sections (BN01 & BN02) did not alter their percent class average. Interestingly, the percent score of individual questions from the examinations with images was significantly higher than the same questions without images. In conclusion, the results from this study indicate that image inclusion in anatomy examinations can have a positive impact on recall of anatomical knowledge and may provide a hint to an answer in anatomy examinations in nursing students. Support or Funding Information None This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".