The influence of spatial ability on high and low order anatomy examination questions in a first year integrated medical curriculum (343.4)
Bibliographic record
Abstract
Students with high spatial visualization ability (Vz) achieve higher grades in anatomy than students with low Vz; however, the influence of Vz on different Bloom’s Taxonomy levels of exam questions has not been established. This study examines the effect of Vz on different Bloom’s Taxonomy levels of anatomy questions. It is hypothesized that students with high Vz will outperform students with low Vz on higher order questions. Participants completed the Mental Rotations Test (MRT) to establish Vz. The mean Vz was 11.5±4.7, which divided participants into high Vz (n=30; mean MRT=15.4±2.7) & low Vz (n=29; mean MRT=7.5±2.5) groups. Exam questions were categorized into 4 Bloom’s levels: knowledge, comprehension, application & analysis. Preliminary data indicate students with high Vz (Vz=90.67±6.91) outperform students with low Vz (Vz=84.55±12.86; p<0.05). Students with high Vz performed better on comprehension level questions (p<0.05) whereas, there was no difference when assessing other exam question levels. This suggests that while high Vz students perform better overall & on comprehension questions, there may not be significant differences on knowledge, application & analysis questions. However, this sample only included 5 knowledge, 8 application & 2 analysis questions indicating that the inclusion of future exam questions may be required to establish significance within the other question levels.
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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 | 0.003 | 0.027 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".