What are the patterns of personal learning environments (PLE) for undergraduate students undertaking degrees in nursing.
Bibliographic record
Abstract
Background - Undergraduate students engage with learning and teaching resources provided by the university to greater or lesser degrees. In doing so, they develop their own personal learning environments (PLE) which may be in addition to, or instead of, that of the university. PLEs have been explained as 'fundamentally a learner-driven model of education, where the traditional provider-centric role of institutions is challenged' (Johnson and Liber, 2008 p3). The limited number of studies examining PLEs to date has focused on the use of Web 2.0 tools and how they are integrated into the student formal and informal learning (Marín Juarros et al., 2014). However, there is little information about the PLEs of students undertaking degrees in nursing. The nature of nursing, with the practical and academic elements, may mean that their personal learning environments are quite different to other subject areas. The notion of what constitutes PLE is unclear and definitions vary. In some contexts a PLE is a digital environment where individual operators choose the inclusion and use of certain digital applications to assist their learning (Marín Juarros et al., 2014). Others, such as Henri, Charlier and Limpens (2008) and Modritscher (2010), define PLEs more broadly as the services, artefacts and other learning resources, gathered from various contexts, used by learners. Within healthcare the digital medium is expanding rapidly and being used more widely in clinical practice (Fahlman, 2013; Tahamtan and Sedghi, 2014). Similarly, an increased use of digital resources is occurring in nursing education (Johnston et al., 2013). Traditionally nursing has be taught in a classroom setting with a didactic approach although teaching methods have been developing to include a variety of strategies, such as problem-based learning, simulation and blended approaches (Barrett and Moore, 2010; McAllister et al., 2013). Nursing students' engagement with different mediums are changing; in part to reflect the technological age (Johnston et al., 2013). The increasing use of technologies is also affecting the way knowledge and practice is learnt (Churchill et al., 2014). Developing a clearer understanding of how, why and with what resources individual undergraduate students develop their PLEs will enhance the integration of different learning sets (such as traditional and online aspects) for the student. There is limited data about the nature of undergraduate PLEs. This study seeks to explore and map the PLEs of undergraduate nursing students in Australia, Canada, England, Hong Kong and Scotland. This will also enable some comparison to be made between the different PLEs of nursing students in different contextual settings. The findings from this study will inform the development of a survey tool for future use to assess personal learning environments of larger cohorts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.003 | 0.001 |
| 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".