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Determinants of nursing students' satisfaction with blended learning
Eman Arafa Hassan1, Ahlam Mahmoud Mohamed2, Fatma Abdou Eltaib3
1Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt. eman_arafa@alexu.edu.eg.
Nursing students' satisfaction with blended learning is influenced by sociodemographic factors, internet access, and environmental support. Educators can use these insights to enhance blended learning experiences and improve student engagement.
Area of Science:
- Nursing Education
- Educational Technology
Background:
- Blended learning is increasingly adopted in nursing education, combining traditional and online methods.
- Student satisfaction is a key concern for the success of blended learning in this field.
- Understanding the factors influencing satisfaction is crucial for optimizing educational strategies.
Purpose of the Study:
- To identify the determinants of nursing students' satisfaction with blended learning.
- To provide insights for educators to improve blended learning environments in nursing programs.
Main Methods:
- A descriptive cross-sectional study was conducted with 1266 nursing students at Alexandria University.
- Data were collected via online surveys using validated scales for satisfaction and environmental factors.
- Backward multiple linear regression analysis was employed to identify significant predictors of satisfaction.
Main Results:
- Sociodemographic variables (age, gender, income), internet access, academic year, computer literacy, learning preferences, and environmental facilitators significantly impacted student satisfaction (p < 0.001).
- The identified variables collectively explained 31% of the variance in blended learning satisfaction scores (Adjusted R² = 0.31).
Conclusions:
- Sociodemographic characteristics, blended learning preference, and perceived environmental support are significant predictors of nursing students' satisfaction.
- Study limitations include self-report bias, convenient sampling, and a cross-sectional design, which restrict generalizability and causal conclusions.
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