Validity of Cognitive Load Measures in Simulation-Based Training
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Cognitive load theory (CLT) provides a rich framework to inform instructional design. Despite the applicability of CLT to simulation-based medical training, findings from multimedia learning have not been consistently replicated in this context. This lack of transferability may be related to issues in measuring cognitive load (CL) during simulation. The authors conducted a review of CLT studies across simulation training contexts to assess the validity evidence for different CL measures. METHOD: PRISMA standards were followed. For 48 studies selected from a search of MEDLINE, EMBASE, PsycInfo, CINAHL, and ERIC databases, information was extracted about study aims, methods, validity evidence of measures, and findings. Studies were categorized on the basis of findings and prevalence of validity evidence collected, and statistical comparisons between measurement types and research domains were pursued. RESULTS: CL during simulation training has been measured in diverse populations including medical trainees, pilots, and university students. Most studies (71%; 34) used self-report measures; others included secondary task performance, physiological indices, and observer ratings. Correlations between CL and learning varied from positive to negative. Overall validity evidence for CL measures was low (mean score 1.55/5). Studies reporting greater validity evidence were more likely to report that high CL impaired learning. CONCLUSIONS: The authors found evidence that inconsistent correlations between CL and learning may be related to issues of validity in CL measures. Further research would benefit from rigorous documentation of validity and from triangulating measures of CL. This can better inform CLT instructional design for simulation-based medical training.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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