Information technology for teaching and learning in a multi-campus public nursing college
Bibliographic record
Abstract
Background: Technologies, such as the use of information technology for teaching and learning, e-learning and virtual learning, are commonly used terms in today’s education system. These ever growing and developing modes of teaching and learning have changed the landscape of higher education, in general. As a result, nursing education has equally responded positively to the use of information technology for teaching and learning. Aim: The aim of this study was to describe and compare the readiness to use information technology for teaching and learning for both nursing students and nurse educators in the two campuses of a North West public nursing college. Setting: The study was conducted in a multi-campus North West public nursing college in South Africa. Methods: A quantitative approach of a comparative descriptive design was followed in this study. Descriptive statistics was analysed using the Statistical Package for the Social Sciences (SPSS) Version 27. Results: A total of 285 (254 nursing students and 31 nurse educators) respondents completed the online questionnaires. Both nurse educators and nursing students were in agreement with the information technology use readiness construct (83.9% and 77.9%, respectively). For all the variables with significant (< 0.05) p -values from the Mann–Whitney U test, the mean ranks were higher for the Ngaka Modiri Molema District (NMMD) campus. Conclusion: When comparing the two campuses, conclusion can be drawn that the campus at NMDD is more ready to use information technology for teaching and learning than the campus at Dr Kenneth Kauda District. Contribution: The results of this study contribute to the body of knowledge on technology use for teaching and learning in nursing education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".