Herwin et al. (2022) conducted a quantitative analysis using a survey research design to evaluate and measure student satisfaction when undergoing online learning. They examined the relationship between various variables, such as the learning management system (LMS), administrative services, lecturer performance, and student satisfaction in learning. The results of the study showed that:
- Lecturer performance is the variable that most affects student satisfaction in online learning.
- Administrative services provide positive value to student satisfaction.
- The LMS does not show a direct effect on student satisfaction. However, it has an indirect effect on lecturer performance and administrative services.
Structural equation modelling (SEM) is a statistical technique used to examine complex relationships between variables. Some advantages of using SEM in research include:
- Testing complex models: SEM allows researchers to test complex models with many variables and relationships. This can help identify the most important factors that influence a particular outcome.
- Confirmatory factor analysis: SEM can test the validity and reliability of measurement instruments, such as surveys or questionnaires. This can help ensure that the data collected is accurate and reliable.
- Hypothesis testing: SEM can be used to test hypotheses about relationships between variables. This can help identify the most important variables in explaining a particular outcome.
- Model fit: SEM provides a measure of model fit. It can help researchers to determine whether their model fits the data.
- Mediation and moderation: SEM can be used to test for mediation and moderation effects. SEM can help identify the mechanism by which variables influence each other.
Overall, SEM is a powerful tool for researchers who want to test complex models and relationships between variables. Herwin et al. (2022) used this tool to help evaluate and measure student satisfaction with online learning. More details can be read in the article below.
This study aimed to evaluate structural models and measurement models of student satisfaction in online learning. This was a quantitative study using a survey research design. Structural model testing was done by examining the relationship between several variables. The variables in question were the learning management system (LMS), admin services, the performance of facilitator lecturers and student satisfaction in learning. The sample used in this study were 149 students. Data analysis was performed using the multivariate Structural Equation Modeling (SEM) technique. The findings of this study indicated that the facilitator lecturer performance is the variable that has the greatest effect in increasing student satisfaction in online learning. Admin service is another variable that has a positive effect on student satisfaction, both direct and indirect effects. The LMS variable does not show a direct effect on student satisfaction, but the LMS has an indirect effect on student satisfaction through the variable facilitator lecturer performance and admin services.
Evaluation of structural and measurement models of student satisfaction in online learning
Herwin Herwin, Fathurrohman Fathurrohman, Wuri Wuryandani, Shakila Che Dahalan, Suparlan Suparlan, Firmansyah Firmansyah, Kurniawati Kurniawati
by: I. Busthomi
