The Influence of Sense of Community and Satisfaction With E-Learning and Their Impact on Nursing Students’ Academic Achievement
Bibliographic record
Abstract
The COVID-19 pandemic has caused a sudden shift to distance learning. For many nursing students, distance learning is a new experience and an essential requirement if they hope to complete their programs. Two challenges that nursing students could face during e-learning are the lack of social presence and low satisfaction. This study aimed to assess students’ sense of community and satisfaction during e-learning and determine their impacts on academic achievement. This cross-sectional descriptive study used convenience sampling to collect data via a student satisfaction survey and a classroom community scale. There was a positive and significant correlation between the sense of community, total satisfaction with e-learning (p < .001), and academic achievement (p < .001). Academic achievement was positively and strongly correlated with satisfaction with teaching (p < .001), assessment (p < .001), generic skills and learning experiences (p < .001), and total satisfaction with e-learning (p < .001). Students who worked collaboratively with their classmates and were more engaged in their learning were more satisfied with e-learning and had higher academic achievement (p < .01). Female participants reported a strong sense of community and satisfaction with e-learning and greater academic achievement than males. Junior students perceived higher satisfaction scores and greater academic achievement (p < .01) than senior students. The findings of this study suggest that failing to meet student expectations can lead to low levels of student involvement. Students’ engagement and satisfaction are good indicators of the quality and effectiveness of online programs.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".