Utilization of Nursing Education Progressive Web Application (NEPWA) Media in an Education and Health Promotion Course Using Gagne’s Model of Instructional Design on Nursing Students: Quantitative Research and Development Study
Bibliographic record
Abstract
BACKGROUND: concept could support the learning processes of nursing students. Nonetheless, it is still necessary to conduct further research on its potential as an information media that supports learning using 1 of the mobile learning methods. OBJECTIVE: This study aims to develop and use the Nursing Education Progressive Web Application (NEPWA) media in an education and health promotion course for nursing students. METHODS: This is a research and development study aimed at developing the NEPWA media using the Analyze, Design, Develop, Implement, and Evaluate approach and a quantitative research with descriptive and pre-experimental 1-group pretest-posttest design conducted in the Study Program of Nursing Sciences, Faculty of Health Sciences, Muhammadiyah University of Surakarta. A total of 39 nursing students in their second year of undergraduate studies participated in this study. A pretest-posttest design was used to measure any changes in the dependent variable, whereas a posttest design was used to measure any changes in the independent variables. RESULTS: <.001; 95% CI 23.88-33.14). In terms of student satisfaction with the learning process using Gagne's model of instructional design, most of the students were satisfied, with a mean score of ≥3. In addition, the results of the measurement using the System Usability Scale on the NEPWA media showed that NEPWA has good usability and it is acceptable by users, with a mean score of 72.24 (SD 8.54). CONCLUSIONS: The NEPWA media can be accepted by users and has good usability, and this media is designed to enhance student knowledge.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".