What are the experiences of student nurses with online learning? Do they have the necessary digital and technological competencies?
Bibliographic record
Abstract
Background and objective: Student Nurses have been conventionally and predominantly taught face to face for several decades. A recent surge in teaching student nurses online in the last decade has been expedited by the onset of the covid-19 pandemic. A significant number of research on online learning, focuses on its effectiveness from an educator’s perspective. Exploring student nurses’ experiences with online learning in relation to their digital and technology skills readiness is pertinent to informing a student-led pedagogy. The research aims to explore the experiences of student nurses with online learning and if they are digital and technology skills ready for online learning or not.Methods: The study is a descriptive qualitative research, which utilises Interpretative phenomenological analysis and hermeneutic Interpretative phenomenology. Four pre-registration student nurses in a university in the South-East of England were recruited for the study. Individual face-to-face tape recorded semi-structured interviews were conducted with verbal and written consent from participants. Data collected was concurrently transcribed and analysed. Preliminary codes were given to the collected data to describe the contents. Interviews were then searched for patterns in the given codes from the transcripts. The themes that emerged were reviewed and refined with written up verbatim quotations from participants to support interpretations. A reflexive diary was kept by the researcher throughout the research, to reduce the likelihood of biases. Results: The themes that emerged from the collected and analysed data indicated that student nurses were digital and technology skills competent to engage in online learning. Online learning was deemed beneficial by all students although a preference for face-to-face learning was reported. Factors that inhibited students from fully engaging with online learning included internet hitches and the inability of nurse educators to use technology. A lack of effective communication between lecturers and students during online learning also marred the experiences of students. Environmental distractions at home and a lack of support from peers and lecturers during online learning were further cited as inhibitors for online learning.Conclusions: The Nursing and Midwifery Council (NMC) requires qualified nurses to have sound technology skills for care delivery. Considerations for online learning must include a prior technology skills competence assessment. The approach will ensure a level playing field for all students who engage in online learning. The appropriate support and interventions can be put in place for students who may not have the prerequisite level of technology skills to engage in online learning. Findings supports a blended learning approach with a student led digital and technology skills baseline assessment, prior to online learning. The approach will ensure a successful co-creation with an amalgamation with pedagogy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".