Aha! Taking on the myth that simulation‐derived surprise enhances learning
Bibliographic record
Abstract
OBJECTIVES: This paper aims to discuss the recurring education-related issue of the high-fidelity simulation myth. In the current instantiation, educators erroneously believe that trainees benefit from authentic uncertainty and surprise in simulation-based training. METHODS: We explore the origins of this myth within the experiential learning and social constructivism theories and propose an evidence-based solution of transparent and guided instruction in simulation. RESULTS: Constructivist theories highlight meaning making as the benefit of inquiry and discovery learning strategies. Inappropriate translation of this epistemology into an element of curriculum design creates unfortunate unintended consequences. CONCLUSIONS: We propose that the translation of constructivist theories of learning within simulation-based education has resulted in a pervasive myth, which decrees that scenarios must introduce realistic tension or surprises to encourage exploration and insightful problem solving. We argue that this myth is masquerading as experiential learning. In this narrative review, we interpret our experiences and observations of simulation-based education through our expertise in education science and curriculum design. We offer anecdotal evidence along with a review of selected literature to establish the presence of this previously undetected myth.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".