Learner characteristics involved in visuospatial anatomy comprehension (535.1)
Bibliographic record
Abstract
The practical aspect of human anatomy instruction is becoming less dissection based and increasingly more reliant on computerized, three‐dimensional (3D) teaching tools. Some students find it difficult to learn from these tools and it is important not to alienate this group of learners. Previous studies have demonstrated that the spatial relations subcomponent of spatial ability is predictive of success in 3D anatomy comprehension, but the effect of other spatial ability subcomponents has not been examined. This study explored the role of three subcomponents of spatial ability and the effect of prior domain knowledge in comprehending a 3D teaching tool. By adding more spatial ability subcomponents and a knowledge component the results may provide a broader picture of the relevant learner characteristics involved in comprehending these tools. 60 students from Western University were recruited. Participants performed an anatomy knowledge test (AKT), three standardized spatial tests, and a pre and post spatial anatomy task (SAT) with a 3D teaching tool as an intervention. Preliminary data analysis (N=24) suggests that participants’ scores on the AKT and the three spatial ability tests are predictive of learning from the 3D teaching tool (p<0.01). These results may have implications for the appropriate design and implementation of computerized 3D instructional animations. Grant Funding Source : Lippincott Williams Wilkins/AAA Education Research Scholarship
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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 | 0.000 | 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".