Facilitation strategies used in e-learning by nurse educators in Rwanda
Bibliographic record
Abstract
Knowledge and skills for teachers to deliver course contents in an e-learning environment is essential. Information Communication Technology (ICT) is being increasingly used in tertiary education as it is flexible and offers many possibilities to meet the needs of a large number of learners. The implementation of e-learning platforms in Rwanda in 2012 for nursing and midwifery instruction has had a positive impact on the quality of nursing education. Educators' facilitation skills play an important role in motivating students in the computer-mediated learning environment. The aim of this paper is to explore the facilitation strategies used in e-learning by nurse educators in Rwanda. A non-experimental quantitative design was used, with 44 nurse educators from three campuses completing the research instruments. The results from this study indicated that the majority of the participants (84.1%) had the same vision of integrating ICT in teaching and learning as their colleagues, the institutional administration, and other staff. 97.7% used computers and/or the internet to prepare lesson and deliver instructions 95.5% reported using facilitation strategies of self-directed learning, 93.2% case studies, 88.6% group discussions, 81.8% small group activities, 72.7% formal lectures, 70.5% role play, 68.2% brainstorming, 63.6% situations of integration, and 63.6% videos. An average of 50% reported using research, and 43.2% workbooks. 27.3% used projects, 25% core lectures, and 11.4% Portfolio. E-learning requires a comprehensive approach of incorporating ICT in teaching and learning. The success of e-learning does not only depend on technological tools available, but also on the pedagogical design, with teachers being required to use innovative teaching approaches to deliver their course contents.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".