Conceptualisation of e-learning in nursing education in the context of Rwanda
Bibliographic record
Abstract
Technological innovations such as e-learning are rapidly transforming the ways that institutions of higher education teach and students learn. In nursing education, e-learning offers opportunities to reach a great number of students, irrespective of time and space. In the context of Rwanda, the implementation of e-learning in nursing education was motivated by the country’s need to upgrade the level of working nurses and midwives at a large scale and in a short period. This study aims at exploring the conceptualization of e-learning in nursing education, in the context of Rwanda. Grounded theory paradigm directed this study based on the Framework from Straus and Corbin (2008). The sample size was comprised of 40 participants: 18 nurse educators, 17 nursing students, two ICT managers, and three experts in the fields. The results indicated that e-learning was conceptualised as (a) a mechanism to advance a political agenda; (b) a tool to open access to education for working nurses and midwives; (c) a student-centred approach, (d) a blended learning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".