Augmented reality m-learning to enhance nursing skills acquisition in the clinical skills laboratory
Bibliographic record
Abstract
Purpose – This paper aims to report on a pilot research project designed to explore if new mobile augmented reality (AR) technologies have the potential to enhance the learning of clinical skills in the lab. Design/methodology/approach – An exploratory action-research-based pilot study was undertaken to explore an initial proof-of-concept design in using AR resources to supplement clinical skills lab teaching. A convenience non-probability sample of 72 undergraduate nursing students tested these resources during lab sessions, and participated in post-exposure surveys and focus groups to help evaluate them. This pilot design aimed to test logistics and gather information prior to further developmental work. Findings – Key similarities emerged between the survey and focus group findings regarding the technical issues and support for student learning. Students clearly expressed a comfort with the technology, and both students and faculty identified the ability to access resources to support self-directed learning and review of skills as positive attributes of using AR. However, technical issues such as slow response times and incompatible smartphones interfered with resource access and frustrated some students, potentially having a negative impact on their learning. Students gave positive feedback regarding the value of mobile access and having AR resources available “at the bedside” where they were practicing. Research limitations/implications – This empirical pilot study was limited to a small number of participants in a single location. However, a deeper understanding of the potential value of AR in clinical health professional education, and best practices in implementing these new technologies, was achieved. Practical implications – This study provides a valuable practical contribution, as the approach for AR resource development described can be readily replicated by teachers with limited technical skills. The practical limitations of AR technologies discovered by use in real-world settings will provide developers and educators with valuable information as they begin to explore the use of AR in the lab and beyond. Social implications – AR represents a rapidly developing field, with increasing social impact. This study provides some initial ideas that will help inform future uptake of AR in wider educational settings, beyond health professional education. Originality/value – This study represents original work in the field, and specifically, an original implementation of AR in an educational context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".