Interactive Video Technology as A Mode of Teaching: A Qualitative Analysis of Nursing Students’ Experiences at A Higher Education Institution in Namibia
Bibliographic record
Abstract
Interactive video technology (IVT) remains one of the common modes of teaching utilised by various higher education institutions (HEIs) across the globe with an aim of catering to ever-increasing educational demands. The objectives of this study were to explore and describe the experiences of nursing students on the use of IVT as a mode of teaching General Nursing Science with a view to describing the aspects that affect their learning. The study was conducted at one of the satellite campuses of an HEI located in north-eastern Namibia. In this study, a qualitative, exploratory and descriptive design was used. A total of fifteen nursing students from the Faculty of Health Sciences in the School of Nursing, purposively selected from the population of fourth-year nursing students who were taught via IVT, participated in the study. Data were collected using semi-structured interviews and analysed by means of content analysis. Three main themes subsequently emerged: nursing students experienced the IVT as a beneficial mode of teaching; the use of IVT as a mode of teaching resulted in certain negative experiences for nursing students; and the presence of certain strategies that strengthen IVT as a teaching mode. The study identified both positive and negative student experiences resulting from the use IVT as a mode of teaching. It was therefore concluded that the School of Nursing should continue to use IVT as a mode of teaching, but should put certain interventions in place to strengthen it and to make the learning environment more favourable for students.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| 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".