## 4 Results
### Identification Factors Among Quality of E-Learning Variables
The model for S2 has an R value of 0.727, indicating a strong correlation, with an R-squared value of 0.528, meaning that 52.8% of the variance in S2 is explained by the model. Moving to S3, knowledge of information technology held by the student, Q2 again appears as a significant predictor with a positive coefficient of 0.214. The model's R value is 0.454, indicating a moderate correlation, and the R-squared value is 0.203, indicating that 20.3% of the variance in S3 can be explained by the predictors. In the model for S4, which represents the student's possession of the necessary resources to participate in e-learning, Q4 (e-learning allows for verifying knowledge) is a significant predictor with a coefficient of 0.316. This model has an R value of 0.497 and an R-squared value of 0.247, meaning 24.7% of the variance in S4 is explained by the model. Lastly, for S5, the innovation of e-learning solutions used by the university, Q2 and Q7 (accessibility of e-learning tools) are significant predictors with coefficients of 0.061 and 0.208, respectively. The model for S5 has an R value of 0.591 and an R-squared value of 0.350, meaning that 35% of the variance in S5 is explained by the independent variables. The multiple regression analysis reveals several important insights into how students' perceptions of e-learning are shaped by various quality factors. The results suggest that perceived effectiveness of e-learning (Q2) consistently plays a pivotal role across multiple dimensions of students' assessments, including content acquisition in both traditional and e-learning formats, technological proficiency, resource availability, and innovation. Additionally, other factors such as university support (Q1), knowledge verification (Q4), and tool accessibility (Q7) contribute to shaping specific aspects of students' perceptions, highlighting the multifaceted nature of e-learning experiences. These findings have significant implications for educational institutions aiming to enhance e-learning experiences. By focusing on improving the perceived effectiveness of e-learning and ensuring accessibility and support, universities can positively influence students' overall perceptions and satisfaction with e-learning as a teaching method. The analysis also suggests that enhancing the quality of specific e-learning components, such as knowledge verification and tool accessibility, can further strengthen students' engagement with and positive evaluations of e-learning environments. The factor analysis method was used to group the factors into variables. The normalized Varimax rotation was applied. The Kaiser-Meyer-Olkin test for the adequacy of the correlation matrix is 0.75, which justifies the use of factor analysis in this case. According to the [PERSON] criterion, two factors with eigenvalues greater than 1 should be retained. When applying [PERSON]'s scree plot criterion, it also suggests retaining two factors. The factor analysis results provide a clear understanding of how the various aspects of e-learning quality (Q1-Q14) cluster into two distinct factors, which together explain 64% of the variance in the data. Each factor represents a different dimension of e-learning quality, as indicated by the factor loadings presented in Table 11. The factors were identified using normalized Varimax rotation, which maximizes the variance explained by each factor while maintaining orthogonality, ensuring that the factors remain uncorrelated. Overall, the identified factors explain 64% of the variance. Factor 1 is primarily associated with aspects related to the role and quality of academic teachers in the e-learning process. This factor shows high loadings for variables such as \"Academic teachers are competent\" (0.814), \"Academic teachers are helpful to students\" (0.795), and \"Academic teachers care about the security of students' personal data\" (0.746).