Top‐Down Influences on Visuospatial Human Anatomy Comprehension
Bibliographic record
Abstract
The purpose of this study is to examine how learners’ spatial ability and prior domain knowledge contribute to the comprehension of visuospatial anatomical information from instructional animation. Spatial ability refers to one's capacity to construct, maintain, and manipulate mental representations. Prior domain knowledge refers to one's previously acquired knowledge of human anatomy. Visuospatial anatomy refers to the spatial properties of anatomical structures such as their 3D shape, form, position in the body, and relative location to surrounding structures. In cognitive science there is growing evidence suggesting that the educational value of instructional materials depends on how well their design reflects human cognitive architecture. Educational value depends on whether learners have enough cognitive resources to store and process the information presented. This study will provide objective evidence as to how learner's spatial ability and prior anatomy knowledge influence visuospatial anatomy comprehension. It will also help to determine who will benefit the most from instruction with animation. The results can be used to improve the design and implementation of instructional resources that will augment learning of visuospatial anatomical information for all learners, regardless of their innate spatial ability or prior knowledge of anatomy. Grant Funding Source : University of Western Ontario
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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 | 0.000 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".