Development of the Learning Media Application "BCNC Smart IV" for Nursing Students through the ADDIE Model
Bibliographic record
Abstract
This developmental research aimed to examine the challenges in learning intravenous fluid administration and to develop and evaluate the effectiveness of a learning media in the form of an application for nursing students. The ADDIE Model framework was employed, comprising five steps: 1) Analysis, 2) Design, 3) Development, 4) Implementation, and 5) Evaluation. The sample consisted of 30 third-year nursing students from Boromarajonani College of Nursing, Chiang Mai. The research tools included the "BCNC Smart IV" application developed by the researchers, an in-depth interview guide exploring experiences in learning to care for patients receiving intravenous fluids, a knowledge assessment on intravenous fluid administration, an evaluation of the quality of the application-based learning media, and a satisfaction survey on the use of the BCNC Smart IV application. Qualitative data were analyzed using content analysis, while quantitative data were analyzed using descriptive statistics and the Wilcoxon Signed Rank test. The results showed that after using the BCNC Smart IV application, the sample group had a significantly higher average knowledge level compared to before its use (p-value < 0.0001). The overall evaluation of the quality of the application-based learning media was at a very good level (M = 4.39, SD = 0.41), and the sample group expressed a very high level of satisfaction with the use of the application (M = 4.39, SD = 0.41). Therefore, the BCNC Smart IV application can enhance nursing students' knowledge about intravenous fluid administration, facilitate convenient content review, and be disseminated to benefit nursing education and learning in other institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".