An action research on the application of online teaching in “Infection Control in Nursing Practice” course
Bibliographic record
Abstract
This study adopts the action research method to understand the impact of online teaching on the learning outcomes of working nursing students using the “Infection Control in Nursing Practice” course as the research context. The research design focuses on the learning activity process of online teaching. The study subjects consisted of 55 working nursing students, aged 25-55 years old, all of whom were taking the online courses for the first time. The results of the study show that the semester grades of online teaching courses were better than those of physical courses and the length of important learning activities such as audio-visual materials (AVMs) would best be produced within 5-12 minutes, while the key points of each section would best be explained within 5 minutes of the beginning of the AVMs. Working students may miss out a variety of online learning activities, so a reminder mechanism should be planned to prevent students from missing out learning activities. The teachers are facilitators and advisors in online teaching, so they may plan time outside of learning activities, such as office hours, to provide a channel for student consultation. The results of this study can be used as a direction to improve the subsequent online teachings and provide a reference for other teachers to implement online teaching.
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".