Undergraduate nursing students' perceptions of active learning strategies: A focus group study
Bibliographic record
Abstract
BACKGROUND: Active learning strategies have been identified as promoting critical thinking, strengthening clinical reasoning, and supporting the transfer of theoretical knowledge to practice amongst nursing students. AIM: This study aimed to understand the undergraduate nursing students' perceptions of the active learning strategies being used in the classroom and to identify critical elements within their learning spaces which contribute to their learning. DESIGN: Qualitative, focus group study. SETTING: A four-year undergraduate baccalaureate nursing program in the Middle East. PARTICIPANTS: 50 undergraduate nursing students selected through purposive and snowball sampling participated in the study. METHODS: Five focus group sessions were conducted with 10 participants in each session. Data collected from the discussions were transcribed and thematically analyzed and aligned with the Taxonomy of Significant Learning. RESULTS: Study results show that undergraduate nursing students affirm that the use of active learning strategies supports the acquisition of foundational understanding, application and integration of knowledge, caring about the learning process, learning to learn, and the human dimension of learning. Participants also identified how best active learning strategies should be utilized and aspects of learning spaces that promote learning. CONCLUSIONS: Although the use of active learning strategies positively enhances the learning process, it is important to ensure that strategies are intentionally integrated into the classroom and aligned with the expected learning outcomes. Considerations of the learning space used are also of importance.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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 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".