Cognitive Load Theory for the Design of Medical Simulations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
STATEMENT: Simulation-based education (SBE) has emerged as an effective and important tool for medical educators, but research about how to optimize training with simulators is in its infancy. It is often difficult to generalize results from experiments on instructional design issues in simulation because of the heterogeneity of learner groups, teaching methods, and rapidly changing technologies. We have found that cognitive load theory is highly relevant to teaching in the simulation laboratory and a useful conceptual framework to reference when designing or researching simulation-based education. Herein, we briefly describe cognitive load theory, its grounding in our current understanding of cognitive architecture, and the evidence supporting it. We focus our discussion on a few well-established cognitive load effects with examples from simulation training and recommend some instructional applications with theoretical potential to improve learning outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it