Aha! Taking on the myth that simulation‐derived surprise enhances learning
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it