Nursing and Midwifery Students’ Perceptions and Experiences of Using Blended Learning in Rwanda: A Qualitative Study
Bibliographic record
Abstract
Background: Although blended learning (BL) is being adopted in public and private higher learning institutions (HLIs) in Rwanda, little is known about students' use of BL in their learning activities. This article describes a qualitative descriptive study of students' perceptions and experiences of BL in Rwanda's post-secondary nursing and midwifery programs in public and private HLIs. Methods: Thirty-three nursing and midwifery students from all public and private HLIs in Rwanda exposed to BL were invited to participate in three online focus group discussions (FGDs) conducted using a developed FGD guide with open-ended questions. Inductive content analysis was used to analyze the transcripts. Results: Three main themes emerged from the data analysis:(1) BL perceived as a new and effective teaching and learning approach, (2) Contextual challenges to the BL method, and (3) Recommendations to improve the BL method. From students' experiences, the benefits included but were not limited to the flexibility of the approach, time, and cost-saving. However, several challenges were identified, including technological issues such as lack of ICT skills and poor internet connectivity. Conclusion: This study provides insights into the usefulness of BL in HLIs and offers recommendations on how BL teaching and learning can be improved to strengthen nursing and midwifery pre-service education quality.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".