Humanising the curriculum: The role of a Virtual World
Bibliographic record
Abstract
Objective : Technology has changed our world; changed the way we communicate, the way we do business and the way education is delivered. As a result, undergraduate student cohorts come to university equipped with new technology, and educators need to transform the delivery of the curricula to satisfy a variety of learning styles. Nursing education, in particular, is developing and transforming to incorporate technology into the learning environment. Clinical placement opportunities are often sparse and alternative experiences need to be considered. Across nursing curricula, it has been recognised that technology has the capacity to provide real-life learning experiences that promote student engagement and meet the learning needs of a diverse student cohort. Methods : This paper will discuss the development of a “Virtual World” in an undergraduate nursing program in Western Australia. The Virtual World initiative is designed to support students to understand the holistic, health-centred intent of the curriculum. Results : Initial results have shown that the Virtual World and humanising the curriculum, has increased learner engagement, improved critical thinking and decision-making. It has enhanced and maintained a high level of student satisfaction and self-efficacy as well as assisting the development of graduate nurses who perceive themselves as health advocates, problem-solvers and organisers of care. Research will continue to follow the use of the Virtual World model, incorporating a virtual family and its integration into the undergraduate nursing curriculum. Conclusions : In the current climate of nurse education and due to a reduction in availability of clinical placements, alternative authentic experiences need to be offered. The development of the Virtual World has enabled meaningful participation in a safe and supportive learning environment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".