Assessing the Relationship Between Students’ Approaches to Learning, Visuospatial Abilities, and Performance in an Undergraduate Human Anatomy Course
Bibliographic record
Abstract
Learning is difficult to quantify. Several metrics for successful student learning have been explored, the most popular of which is academic performance. However, academic performance does not always accurately represent nuanced outcomes such as meaningful learning and skill development, nor is it a successful predictor of long‐term knowledge retention. One metric that has demonstrated alignment with such qualitative learning outcomes is student approach to learning; a framework that evaluates the depth with which students interact with their learning environment and the course content. However, the student approach to learning framework has yet to be adequately investigated for its reliability as a metric for learning in human anatomy, specifically. One measure that has been shown to predict learning achievement in human anatomy is visuospatial ability. Individuals with high visuospatial abilities are typically faster and more successful at performing three‐dimensional anatomy tasks than those with low visuospatial abilities. It has been suggested that this may be due to differences in strategies used for learning by students with high (versus low) visuospatial abilities. Despite this, the degree of correlation between students’ approaches to learning and visuospatial abilities has yet to be investigated. The present study therefore aimed to examine the relationship between students’ approaches to learning, visuospatial abilities, and performance in anatomy to investigate the validity of the student approach to learning framework as an alternative metric to evaluate anatomy learning. Physical Therapy, Occupational Therapy, and Kinesiology students (n = 35) completed a Mental Rotations Test and Revised 2‐factor Study Process Questionnaire after the end of their first anatomy course. Responses and final course grades were examined using regression analyses to detect their degrees of correlation. We hypothesized that visuospatial ability would correlate positively with both anatomy performance and deep approach to learning scores and have a negative correlation with surface approach to learning scores. However, no significant correlations were found between visuospatial ability and deep or surface approach to learning scores at p ≤ 0.05. Anatomy performance was positively correlated with deep approach to learning scores ( β = 0.427, p = 0.02) and negatively correlated with surface approach to learning scores ( β = ‐0.704, p < 0.01) but had no significant correlation with visuospatial ability at p ≤ 0.05. The data suggest that visuospatial ability does not significantly influence students' approaches to learning; however, additional research on larger populations is required to explore these relationships further.
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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 | 0.000 |
| 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 | 0.001 |
| 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".