Gamification: A pedagogical strategy for generation Z nursing students
Bibliographic record
Abstract
Background: Nurse educators must develop teaching strategies to meet the growing needs of healthcare and engage Generation Z nursing students. Gamification based learning activities, such as educational escape rooms, combine multiple modalities that appeal to the current generation of nursing students. Educational escape rooms have the potential to improve teamwork and achievement of learning outcomes among nursing students. However, few studies have been done to identify nursing student perception of the educational and teambuilding value of these activities. Methods: A longitudinal exploratory study was conducted to determine baccalaureate nursing student perceptions of the value of team-based escape rooms as an educational tool and compare self-reported teamwork scores after completing a series of escape activities. Students were recruited from a baccalaureate nursing course to participate in five escape room activities and asked to complete anonymous online surveys to determine perceptions of educational value and teamwork after each activity.Results: A total of 33 participants responded positively to educational escape rooms with 100% of participants stating they would recommend this activity to others. Teamwork assessments were also positive with improvement as the study progressed.Conclusions: Educational escape rooms are an appropriate tool for nurse educators today that students value and enjoy. The use of escape rooms in nursing education engages students in learning while also improving teamwork.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".