A case study for evaluating nursing and health sciences student satisfaction with e-learning
Bibliographic record
Abstract
An increased demand for e-learning in nursing and health sciences education is emerging and not enough is known about factors determining nursing student satisfaction with e-learning, therefore studies clarifying how to design and implement efficient e-learning programs to increase student satisfaction are necessary. This case study helps fill this gap by investigating the factors influencing nursing students??? satisfaction with e-learning and the relationship between satisfaction and GPA in healthcare higher education. A web-based questionnaire collected data on various aspects of e-learning and 140 students from University of Ontario Institute of Technology participated. Statistical analysis was completed and responses to open-ended questions were explored using thematic open-coding. Results revealed the most highly influential factors on nursing student satisfaction being perceived liking and perceived usefulness, while usability and communication & teaching factors having less predictive power to the student satisfaction. Another important finding is that any potential student that wants to pursue studies in a health related program in the university where e-learning is part of the program, who has a high level of satisfaction will be able to obtain a good GPA. Major barriers in using e-learning were identified in the area of communication, course management, feeling of being disconnected, and technology issues. In conclusion, we believe the findings of this study add a new perspective on satisfaction factors with e-learning for nursing students and describe the link between their satisfaction level and GPA. Further research is required to explore how e-learning program design can address the barriers to e-learning identified in this study and further explore the conclusions of this study to other nursing and health sciences programs at other universities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".