Teaching Modalities and Their Effect on Nursing Students Knowledge and Satisfactions
Bibliographic record
Abstract
Nursing education plays a crucial role in preparing healthcare professionals to provide high-quality care in different healthcare settings. The acquisition of knowledge and the development of nursing education, be it online or face-toface, is crucial to ensure that students are competent enough to address the complex challenges of the healthcare work environment. Aim: The primary aim of the study is to compare the knowledge and satisfaction of students pursuing online and lecture-based diploma courses on adult health nursing studies in South Jordan. Methods: Quantitative research was employed, involving a sample from 200 diploma nursing students enrolled across 3 nursing institutions across South Jordan. Two questionnaires were conducted to compare knowledge derived from the syllabus and the content of the courses, and comparison of satisfaction of the course by different teaching styles. A set of multiple-choice questions were asked to students to examine their knowledge about courses and make subsequent comparisons between lecture-based and online courses. Data was collected through surveys to gauge their knowledge along with their satisfaction levels, to evaluate their perceptions about learning experiences. Results: The findings indicated that 79% of students rated face-to-face learning above average, whereas the online learning group showed lower scores amongst the two with 22% reported good results. Also, satisfaction surveys revealed that 69% of students in the face-to-face learning group reported higher levels of satisfaction, primarily because of the feasibility of direct interaction with instructors and peers in the learning environment. Conclusion: The demand for nursing professionals in Jordan continues to grow, entailing the need to explore innovative approaches to education that effectively facilitate knowledge acquisition. Even though online lectures are important for students, the ones attending face-to-face lectures provided more competency and depth in their knowledge when contested for.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".