E‐Learning: Effective or Defective? The Impact of Commercial E‐Learning Tools on Learner Cognitive Load and Anatomy Instruction
Bibliographic record
Abstract
The rising popularity of commercial anatomy e‐learning tools has occurred, in part, due to increasing class sizes and a reduction in anatomy laboratory hours at many educational institutions. Using a dual‐task methodology, we examined two anatomical e‐learning tools (Netters 3D and ADAM Interactive) to determine the effect of their design on cognitive load. We hypothesize that longer reaction times on a modified Stroop secondary task would indicate a higher cognitive load imposed by the primary task (Anatomy Software), which would interfere with learning. Learners (n=7) were assessed using a baseline anatomy knowledge test, secondary task response times, and an anatomy knowledge post‐test. Results showed that when students use ADAM, they have significantly longer reaction times on the secondary task than when they use Netters (1525ms±433 and 1060ms±56 respectively, p=0.039). Ironically, students appeared to perform better on post‐test measures when using ADAM than when using Netters (3.17±1.83 and 1.33±1.21 respectively, p=0.130). These results suggest that ADAM, which is a simplistic 2‐dimensional e‐learning tool, sustains learner attention and facilitates learning more effectively than Netters 3D, a 3‐dimensional interactive e‐learning platform. A study is underway with 50 participants to determine if the trends seen in the initial experiment are consistent within a larger student population.
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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 | 0.005 | 0.045 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".