The Effect of Image Quality on Anatomy Learning
Bibliographic record
Abstract
Illustrations are critical learning tools in anatomy education. However, evaluation of anatomy image quality is entirely subjective and images display an immense variance in complexity and clarity. The objective of the current study is to determine whether image quality impacts learnability of human anatomy . Undergraduate students with no previous anatomy and physiology background undergo a learning phase where they review two paper‐based instructional modules on independent anatomical material (human hand and eye). There are two versions of each module that vary in image quality, but contain identical text. One version contains low‐quality images the other high‐quality images. The high‐quality images had enhanced visual contrast, label clarity, colour‐coding and consistency in style and anatomical view across images. Immediately following the learning phase, participants are tested on their comprehension of the modules using either 2‐dimensional illustrations or anatomical specimens. Two delayed comprehension assessments are completed 24 and 48 hours later to determine long‐term learning outcomes. We predict higher comprehension scores when material is presented using high‐quality images on both immediate and delayed tests, and on both illustration‐based and specimen tests. Higher quality images will reduce any cognitive demands geared towards identifying and processing critical visual features and structures. As a result, more cognitive resources can be dedicated to consolidating presented information and integrating this new knowledge with pre‐existing information for stable, long‐term learning. This study will help guide medical illustrators to create educational material that maximizes learning.
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".