Task complexity and cognitive load in simulation‐based education: A randomised trial
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: When designing simulation for novices, educators aim to design tasks and environments that are complex enough to promote learning but not too complex to compromise task performance and cause cognitive overload. This study aimed to determine the impact of modulating task and environment complexity on novices' performance and cognitive load during simulation. METHODS: Second-year pharmacy students (N = 162) were randomly assigned to one of four conditions (2 × 2 factorial design) in simulation: simple task in simple environment, complex task in simple environment, simple task in complex environment and complex task in complex environment. Using video recordings, two raters assessed students' performance during the simulation. We measured intrinsic cognitive load (ICL) and extraneous cognitive load (ECL) with questionnaires after the task and tested knowledge after task and debriefing. RESULTS: Mean performance scores in simple environment were 28.2/32 (SD = 3.8) for simple task and 25.8/32 (SD = 4.2) for complex task. In complex environment, mean performance scores were 24.6/32 (SD = 5.2) for simple task and 25.6/32 (SD = 5.3) for complex task. We found significant interaction effects between task and environment complexity for performance. In simple environment, mean ICL scores were 4.2/10 (SD = 2.2) for simple task and 5.7/10 (SD = 1.5) for complex task. In complex environment, mean ICL scores were 4.9/10 (SD = 1.8) for simple task and 5.1/10 (SD = 1.9) for complex task. There was a main effect of task complexity on ICL. For ECL, we found neither an interaction effect nor main effects of task and environment complexity. There was a main effect of task complexity on knowledge test after task and main effects of both task and environment complexity on knowledge after debriefing. CONCLUSIONS: Performance was good, and cognitive load remained reasonable in all conditions, which suggests that, despite increased complexity, students seemed to strategically manage their own cognitive load and learn from the simulations. Our findings also indicate that environmental complexity contributes to ICL.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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