Managing cognitive load in simulations: exploring the role of simulation technologists
Bibliographic record
Abstract
BACKGROUND: Facilitating simulation is a complex task with high cognitive load. Often simulation technologists are recruited to help run scenarios and lower some of the extraneous load. We used cognitive load theory to explore the impact of technologists on instructors, identifying sources of instructor cognitive load with and without technologists present. METHODS: Data were collected from 56 simulation sessions for postgraduate emergency medicine residents. Instructors delivered 14 of the sessions without a technologist. After each session, the instructor and simulation technologist (if present) provided quantitative and qualitative data on the cognitive load of the simulation. RESULTS: Instructors rated their cognitive load similarly, regardless of whether simulation technologists were present. However, the composition of their cognitive load differed. Instructors experienced reduced cognitive load related to the simulator and technical resources when technologists were present. Qualitative feedback from instructors suggested real consequences to these differences in cognitive load in (1) perceived complexities in running the scenario, and (2) observations of learners. CONCLUSION: We provide evidence that simulation technologists can remove some of the extraneous load related to the simulator and technical resources for the instructor, allowing the instructor to focus more on observing the learner(s) and tailoring the scenario to their actions.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".