Determining optimal learning conditions for acquiring spatial 3D information using computer‐based anatomical reconstructions
Bibliographic record
Abstract
Interactive stereoscopic 3D digital models of head and neck structures, generated from CT scans of a male cadaver, have been developed. The display and interactive features of the models will be used to determine optimal conditions for acquiring 3D information for individuals with different spatial abilities. A pre‐mental rotation test will be used to determine participants' spatial ability. Based on pre‐test results, participants will be assigned to either the high or low spatial ability group. Members of each group will be further divided into four subgroups based on four learning conditions. A stereo/high‐interactive group will study anatomy using stereoscopic models and will have active control over the pace and direction of the model. A non‐stereo/low‐interactive group will examine anatomy with 2D images and restricted interaction. A stereo/low‐interactive and non‐stereo/high‐interactive group will study the same anatomy in corresponding conditions. Learning will be assessed by 50 multiple‐choice questions that require mental manipulation of internalized 3D anatomical representations. The results of this study will aid the design and implementation of effective 3D computer visualizations to help students comprehend the spatial 3D organization of the human body, while accounting for individual differences in spatial abilities. Grant Funding Source Internal
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".