The Use of WhatsApp as An Educational Communication Tool in Higher Education: Experiences of Nursing Students in Kavango East, Namibia
Bibliographic record
Abstract
WhatsApp is the most popular networking site used by most university students for general purposes, and as a communication, collaborative and transactional tool in the teaching and learning process. However, experiences of its use among university students as an educational communication tool in low and resource-constrained settings have not been explored. Following a qualitative, descriptive, phenomenology approach, this study described and explored nursing students’ experiences of the use of WhatsApp as an educational communication tool. Data were collected from 24 university nursing students who were conveniently sampled to participate in the focus group discussions; thereafter, data were analysed using qualitative content analysis. Whittemore, Chase and Mandle’s primary criteria of validity in qualitative research, which include credibility, authenticity, criticality and integrity, were used to ensure the quality of the study. Ethical approval and permission were granted by the School of Nursing Research Ethics Committee. Informed consent was obtained from participants, and their anonymity and confidentiality were ensured. The findings revealed that WhatsApp is a beneficial communication tool but has effects on human behaviour. Moreover, connectivity and handset-related challenges were experienced by the students. Following these findings, it is concluded that WhatsApp is a suitable communication tool in higher education and in maintaining communities of practice among students and lecturers. Conversely, there is a need to educate students on mechanisms to mitigate its negative effects on human behaviours, such as disturbances, addiction, and lack of responses. Lastly, universities should consider partnering with network providers to improve connectivity among students and lecturers, as well as accessibility to affordable smartphones.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".