Immersive learning in nursing education: Results of a study
Bibliographic record
Abstract
Objective: This study evaluated the effectiveness of an immersive teaching and learning approach for undergraduate nurses. Traditional classroom tutorials were combined with self-directed learning using LabTutor™, an online learning platform, and clinical nursing simulation using high fidelity manikins. Learning modules were designed to link the student’s knowledge and understanding of biosceince with clinical assessment and nursing management in order to develop clinical decision-making skills. It was anticipated that students’ learning experience would be enhanced by the higher level of realism that is possible using the sophisticated manikins and authentic patient clinical data and case notes provided in LabTutor™.Methods: The study took place in a New Zealand School of Nursing in 2014. Qualitative data was gathered using focus groups and an external facilitator. Quantitative data was gathered using an online survey.Results: Participants were second year undergraduate nursing students (N = 111): 71 (64%) interviewees, and 82 (73%) survey respondents. Qualitative data showed that the immersive learning process was effective. Quantitative data affirmed that immersive learning was liked, confidence improved, students enjoyed the process, and would recommend it to others. Using simulation and patient case studies were preferred teaching strategies. Performing experiments, and using digital LabTutor™ technology challenged many, but skills improved over the year.Conclusions: The immersive learning approach was effective. However, despite the high level of authenticity made possible by using high fidelity manikins, realism was hard to establish. An unexpected learning outcome occurred when intermittent technology malfunction prompted students to use problem-solving skills.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".