E‐Learning: effective or defective? The impact of commercial e‐learning tools on learner cognitive load and anatomy instruction (725.7)
Bibliographic record
Abstract
Traditionally, educational researchers have focused on Cognitive Load Theory (CLT) to guide the design of novel e‐learning tools. However, there is little evidence to suggest that the design of commercially available e‐learning tools has been guided by educational theories, including CLT. As e‐learning tools gain popularity as methods of instruction in anatomy education, more research is merited to gauge the impact of these tools on learner cognitive load. This study aims to examine three commercially available anatomical e‐learning tools to determine their effect on learner cognitive load. Anatomy students at Western University will be invited to participate in this study, and will be randomly assigned to view all three e‐learning tools; each featuring the anatomy of a selected joint. To quantify the cognitive load that each tool places on the learner, a dual task methodology (a paradigm wherein the learner performs two tasks simultaneously) will be utilized. Learning material from an e‐learning tool will be assigned as a primary task and visual observation will be assigned as a secondary task. Learners will be assessed using a baseline anatomy knowledge test, secondary task response times, and an anatomy knowledge post‐test. We hypothesize that longer reaction times on the secondary task indicate a high cognitive load imposed by the primary task, and will result in poor performance on the knowledge post‐test.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".