Nursing students’ perceptions of effective problem-based learning tutors
Bibliographic record
Abstract
Aim To explore baccalaureate nursing students' perceptions of what makes an effective tutor in problem-based learning courses, and the influence of effective teaching on students' learning and experience. Method Students enrolled in all four years of a baccalaureate nursing programme completed online surveys (n=511) and participated in focus groups (n=19). Data were analysed and combined using content analysis. Findings The data were summarised using five themes, the '5 Ps' of effective teaching in problem-based learning. Nursing students perceived effective problem-based learning tutors to be prepared with knowledge and facilitation skills, person-centred, passionate, professional and able to prepare students for success in the nursing programme. Effective tutors adjusted their approaches to students throughout the four years of the nursing programme. Conclusion Effective teaching in problem-based learning is essential and has significant effects on nursing students' learning, motivation and experience. Important attributes, skills and strategies of effective problem-based learning tutors were identified and may be used to enhance teaching and plan professional development initiatives.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".