Using Role‐Playing Simulations to Teach Endocrine and Respiratory Physiology in Large Classes
Bibliographic record
Abstract
Role‐playing simulations can help students master complex physiological processes, but may be less effective in larger classes if most students are observers instead of participants. The current study addressed this issue by evaluating the effectiveness of dividing the class into support groups for each role‐playing student. Depending on the situation, support groups provided advice verbally or via clickers. The simulations emphasized higher‐order thinking by asking students to predict how the simulation would change under different conditions. The first role‐playing exercise addressed negative feedback in the hypothalamic ‐pituitary‐target gland axis, using instrument noises to represent the hormonal output of the hypothalamus (drum), anterior pituitary (clapper), and target gland (clanger). After practicing the simulation under normal conditions, students worked with their support groups to predict how the simulation would change in response to perturbations such as an altered steady state, dysfunction of the target organ, or a tumor (an audience member playing an additional instrument). The second exercise examined the mechanics of ventilation by asking concentric circles of students to represent the chest wall/diaphragm, pleural membranes, and lungs. Students predicted how the circles would move during normal inspiration and expiration and under disease conditions (pneumothorax, emphysema, pulmonary fibrosis). Mastery of relevant concepts was examined both in the short term, by pre‐ and post‐testing, and in the long term, by examining exam performance, and student perceptions were studied using anonymous surveys. In summary, adding elements of “predict and test” and support groups to role‐playing simulations may increase their utility in larger classes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".