Size Matters? Evaluating the Effect of Size on Anatomy Learning
Bibliographic record
Abstract
Until recently, the size of an anatomical structure for learning was simply its natural size. Now, with increasing accessibility of 3D scanning and printing, a highly accurate model of virtually any size can be produced. But the question remains, what is the best size for learning? To investigate the effect of object size on learning, 3D models were printed from surface scans of bones using a structured light 3D scanner. A human thoracic vertebra and a human hemipelvis were chosen both for their variation in size and the fact that bones lend themselves well to 3D printing. The bones were each printed in PLA filament at 50%, 100%, and 400% scale. Undergraduate students from McMaster University (n=120) with no prior knowledge of anatomy were randomized into six groups according to 1) which size of model they would learn from and 2) which bone they would learn first. Each participant was asked to learn nominal anatomy from both a hemipelvis and a vertebra model of the same size. After the learning stage, participants were immediately tested on a real bony specimen. The learning and testing stages were then repeated with the other bone. Finally, participants completed a mental rotations test (MRT) and operation span task (OSPAN) to control for any effects of individual differences in spatial ability or working memory on anatomy learning, respectively. Participants also completed a short qualitative survey about their opinions on the size, labelling, colour, and handling of the 3D printed models. Data collection is underway. Test performance will be analyzed using a 3 (bone size: 0.5x, 1x, 4x) × 2 (Bone type: vertebra, hemipelvis) factorial ANOVA. This study has been approved by the Hamilton Integrated Research Ethics Board (HiREB). Insight into how model size affects anatomy learning will provide useful information for educators looking at printed 3D and virtual reality models used for educational purposes. Support or Funding Information This study was internally funded by the Education Program in Anatomy at McMaster University. This abstract is from the Experimental Biology 2019 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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".