Computer visualizations: Factors that influence spatial anatomy comprehension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer visualizations are increasingly common in education across a range of subject disciplines, including anatomy. Despite optimism about their educational potential, students sometime have difficulty learning from these visualizations. The purpose of this study was to explore a range of factors that influence spatial anatomy comprehension before and after instruction with different computer visualizations. Three major factors were considered: (1) visualization ability (VZ) of learners, (2) dynamism of the visual display, and (3) interactivity of the system. Participants (N = 60) of differing VZs (high, low) studied a group of anatomical structures in one of three visual conditions (control, static, dynamic) and one of two interactive conditions (interactive, non-interactive). Before and after the study phase, participants' comprehension of spatial anatomical information was assessed using a multiple-choice spatial anatomy task (SAT) involving the mental rotation of the anatomical structures, identification of the structures in 2D cross-sections, and localization of planes corresponding to given cross-sections. Results indicate that VZ had a positive influence on SAT performance but instruction with different computer visualizations could modulate the effect of VZ on task performance.
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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 | 0.000 |
| 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 it