Evaluation of computer visualizations developed for anatomy education
Bibliographic record
Abstract
Introduction The use of computer visualizations in anatomy courses is expanding, but there is little evidence available suggesting that these visuals enhance learning of spatial anatomical information. Purpose To evaluate computer visualizations of the superior mediastinum developed with different display modes and interactive control features. Methods Participants will be students from the University of Western Ontario. General information about the participants, including their gender, program of study, and past experiences with spatial tasks will be collected in a baseline questionnaire. A mental rotation task that has been standardized for university students will be used to determine participants' spatial ability. Next, participants will be randomly allocated to one of three visual groups: stereoscopic 3D, 3D, or 2D. Participants in each visual group will be subdivided into one of two interactive groups: active control or no control. Each participant will complete an identical standardized electronic learning module pertaining to anatomy of the superior mediastinum. The module will have clearly defined learning objectives and will differ only by the visualization and manipulation capabilities of the model. Learning will be assessed by a post‐knowledge test consisting of 50 multiple‐choice questions – half spatially related and half non‐spatially related. The spatial questions are assumed to require manipulation of mental representations, while the non‐spatial questions involve the recognition and identification of anatomical structures. Significance The results will help establish guiding principles that will facilitate the design and implementation of effective and efficient computer visualizations that can be adapted to the individual learner's level of spatial ability. Grant Funding Source : n/a
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".