Using Mobile Learning to Enhance the Quality of Nursing Practice Education
Bibliographic record
Abstract
In this chapter, we first review the research literature pertaining to the use of mobile devices in nursing education and assess the potential of mobile learning (m-learning) for nursing practice education experiences in rural higher education settings. While there are a number of definitions of m-learning, we adopted Koole’s (2005) FRAME model, which describes it as a process resulting from the convergence of mobile technologies, human learning capacities, and social interaction, and use it as a framework to assess this literature. Second, we report on the results of one-on-one trials conducted during the first stage of a two stage, exploratory evaluation study of a project to integrate mobile learning into the Bachelor of Science Nursing curriculum in a Western Canadian college program. Fourth year Nursing students and instructors used Hewlett Packard iPAQ PDAs for a two week period around campus and the local community. The iPAQs provided both WiFi and GPRS wireless capability and were loaded with selected software, including MS Office Mobile, nursing decision-making and drug reference programs. Our participants reported on a variety of benefits and barriers to the use of these devices in nursing practice education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".