The anatomy of <scp>E</scp>‐Learning tools: Does software usability influence learning outcomes?
Bibliographic record
Abstract
Reductions in laboratory hours have increased the popularity of commercial anatomy e-learning tools. It is critical to understand how the functionality of such tools can influence the mental effort required during the learning process, also known as cognitive load. Using dual-task methodology, two anatomical e-learning tools were examined to determine the effect of their design on cognitive load during two joint learning exercises. A.D.A.M. Interactive Anatomy is a simplistic, two-dimensional tool that presents like a textbook, whereas Netter's 3D Interactive Anatomy has a more complex three-dimensional usability that allows structures to be rotated. It was hypothesized that longer reaction times on an observation task would be associated with the more complex anatomical software (Netter's 3D Interactive Anatomy), indicating a higher cognitive load imposed by the anatomy software, which would result in lower post-test scores. Undergraduate anatomy students from Western University, Canada (n = 70) were assessed using a baseline knowledge test, Stroop observation task response times (a measure of cognitive load), mental rotation test scores, and an anatomy post-test. Results showed that reaction times and post-test outcomes were similar for both tools, whereas mental rotation test scores were positively correlated with post-test values when students used Netter's 3D Interactive Anatomy (P = 0.007), but not when they used A.D.A.M. Interactive Anatomy. This suggests that a simple e-learning tool, such as A.D.A.M. Interactive Anatomy, is as effective as more complicated tools, such as Netter's 3D Interactive Anatomy, and does not academically disadvantage those with poor spatial ability. Anat Sci Educ 9: 378-390. © 2015 American Association of Anatomists.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".