The Influence of Spatial Ability On Anatomy Examination Questions in an Integrated Medical Curriculum
Bibliographic record
Abstract
Background Students with high spatial visualization ability (Vz) have been found to outperform students with low Vz in anatomy. However, how Vz influences anatomy performance has not been established. Thus, this study aimed to assess the influence of Vz on medical student performance on different levels of anatomy questions categorized by Bloom's taxonomy levels and discrimination index (DI) and to observe the relationship between Vz and anatomy performance. We hypothesized that there would be a positive correlation between Vz and performance on more difficult exam questions categorized by DI and Bloom's taxonomy. We also hypothesized that there would be a positive correlation between Vz and anatomy written exam, anatomy lab exam, and overall anatomy performance. Methods First year medical students in a systems‐based integrated medical curriculum (n=61), completed the Mental Rotations Test (MRT) prior to the start of anatomy to establish Vz. All anatomy exam questions were categorized into four Bloom's taxonomy domains of increasing difficulty level (identification, comprehension, application, and analysis). These questions were also categorized into three tiers via DI. Results No significant relationship (p>0.05) was found between Vz and questions categorized by DI or Bloom's taxonomy. Data also indicated that although entrance Vz plays an insignificant role in medical student anatomy lab exam, anatomy written exam, and overall performance in the anatomy course, there is a correlation between entrance Vz and anatomy performance in the very first systems‐based module (r 2 =0.017, p≤0.05). Discussion These findings suggest that entrance Vz may influence anatomy performance at the beginning of the curriculum; however, students with lower Vz find ways to cope and increase anatomy performance throughout the curriculum. Due to the significant relationship between Vz and the first systems‐based module, further analysis was completed to assess the relationship between Vz and anatomy question difficulty. This analysis indicated that there was no significant interaction between Vz and questions categorized by DI or Bloom's taxonomy within that first systems‐based module (p>0.05), suggesting that Vz's effect on performance in anatomy may not have a relationship with question difficulty categorized by Bloom's taxonomy or DI. Further research is necessary to explore how Vz influences anatomy performance and how students’ ability to train Vz and change study strategies influences the effect of Vz on anatomy performance throughout the medical curriculum.
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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 | 0.001 | 0.001 |
| 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".