Nursing students’ use of technology enhanced learning: A longitudinal study
Bibliographic record
Abstract
Background: Nurses are expected to be information and communication technology (ICT) literate at registration but, despite the growth of social uses of technology, little is known of the views of students on the use of technology enhanced learning. The purpose of this study was to explore the experience and confidence of first year pre-registration nursing students with information and communication technologies and technology enhanced learning. Methods: A prospective longitudinal survey design with multiple data collection strategies was employed. Cross- sectional surveys using a new ICT and Education Scale at three time-points were used to collect data. The target population were first year UK nursing students (n=458); of those 287 (41.7%) participated in the initial baseline survey. Results: There were three main findings. First, more than half of the respondents (67%) reported that they were not confident with aspects of information literacy and were not prepared for technology enhanced learning (60%) as they entered university and these aspects showed least change through time. Second, from varied starting points at baseline, respondents’ experience and confidence with ICT skills showed different trajectories over time. Third, there was some evidence that demographic profile was related to use and acceptance of ICT in the educational context. Conclusions: There is some evidence that nursing students find it difficult to negotiate the boundaries between their pre-university experience with technologies and that of education and practice. In a rapidly changing technology environment this has implications for curriculum planning in the first six months of a programme of study as well as student/staff preparation for technology enhanced learning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".