Correlating Spatial Ability With Anatomy Assessment Performance: A Meta‐Analysis
Bibliographic record
Abstract
Interest in spatial ability has grown over the past few decades following the emergence of correlational evidence associating spatial aptitude with educational performance in the fields of science, technology, engineering, and mathematics. The research field at large and the anatomy education literature on this topic are mixed. In an attempt to generate consensus, a meta‐analysis was performed to objectively summarize the effects of spatial ability on anatomy assessment performance across multiple studies and populations. Relevant studies published within the past 50 years (1969–2019) were retrieved from eight databases. Study eligibility screening was followed by a full‐text review and data extraction. Use of the Mental Rotations Test (MRT) was required for study inclusion. Out of 2,450 screened records, 15 studies were meta‐analyzed. Seventy‐three percent of studies (11 of 15) were from the United States and Canada, and the majority (9 of 15) studied professional students. Across 15 studies and 1,245 participants, spatial ability was weakly associated with anatomy performance (rpooled = 0.240; CI at 95% = 0.09, 0.38; P = 0.002). Performance on spatial and relationship‐based assessments (i.e., practical assessments and drawing tasks) was correlated with spatial ability, while performance on assessments utilizing non‐spatial multiple‐choice items was not correlated with spatial ability. A significant sex difference was also observed, wherein males outperformed females on spatial ability tasks. Given the role of spatial reasoning in learning anatomy, educators are encouraged to consider curriculum delivery modifications and a comprehensive assessment strategy so as not to disadvantage individuals with low spatial ability.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".