Comparing effects of an escape room-style activity versus a traditional lecture on student learning outcomes in a graduate pre-licensure pediatric nursing course
Bibliographic record
Abstract
Background and objective: Interactive learning enhances nursing education by fostering critical thinking, problem-solving skills, and teamwork. Escape room–style instruction is an innovative but underutilized approach in nursing. Traditional lectures may not fully engage students or optimize content retention. Escape rooms offer many potential benefits; however, their effectiveness in didactic nursing education remains understudied. The aim of this study was to compare traditional lectures and escape room style instruction in a pre-licensure pediatric nursing course to determine differences in learning outcomes. Methods: Comparative observational study compared outcomes in two sections of the same pediatric nursing course in the same quarter which covered identical content using different teaching methods. Content retention was measured through pre-/post-tests. T-test scores and frequency tables showing changes from pre-test to post-test are reported by modality. Results: A total of 61 students participated, with 21 attending a traditional lecture class and 45 attending an escape room class. Escape room cohorts demonstrated significantly higher post-test scores, suggesting improved short-term retention. Conclusions: Escape room style classes can help enhance students critical thinking skills and short-term content comprehension and retention versus a traditional lecture style class. Classroom-based escape rooms can also offer a low-tech, high-impact alternative to traditional simulation labs to actively engage students in learning. However, further research is needed to assess its long-term effects in nursing education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".