Use of a Simulation of the Ventilator-Patient Interaction as an Active Learning Exercise: Comparison with Traditional Lecture
Bibliographic record
Abstract
Research suggests that simulation technology has potential to enhance student achievement, particularly for students having a preference for hands-on learning. The aim of this study was to compare ventilation learning outcomes in students attending traditional lecture versus students using an active learning ventilation simulation. A computer simulation was developed to advance students' learning of mechanical ventilation. Forty-one students were divided into upper and lower strata based on performance rankings and were then randomly assigned to first complete a simulation scenario or view a lecture. Two distinct ventilation topics, controls and clinical, were developed for each instructional method. Students completed examinations three weeks following each respective instructional intervention (lecture or simulation scenarios) as well as one long-term examination and survey six weeks following the second examination. Upper-ranking students who learned the clinical topic through the simulation scenarios outperformed students who learned by traditional lecture. In addition, upper-ranking students scored higher than lower-ranking students in both the clinical and long-term composite examinations. No differences in student scores attributed to instructional method or class rank were identified for the controls topic. Survey results indicated that students were more engaged as learners when using the simulation and wished to have the simulation available during their clinical intensive care unit (ICU) rotations. Use of the simulation was associated with improved performance of upper-ranking students on the clinical-topic exam and was equivalent to lecture as an instructional intervention on the controls-topic exam. The simulation was perceived as an engaging, desirable tool providing immediate feedback.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".