## 4 Content and Results of Research
### Results of Research
#### 4.2.2 Structural Equation Results  
In order to test the proposed model and to analyze the proposed hypotheses, structural equation modeling was used for the analysis. The evaluation indicators of the measurement model are reliability and validity. Reliability was tested using Cronbach's coefficient and combined reliability. Validity was tested by convergent validity. The results of the reliability and convergent validity tests are shown in Table 2.  
The standardized loadings of the observed variables corresponding to the seven latent variables in this study's model are all much larger than the suggested value of 0.5, and all of them reached the significant level, indicating that this scale has good convergence [88]; the combined reliability of the seven latent variables is larger than the suggested value of 0.7, indicating that the measurement model of this study has good internal consistency; and the average variance extracted from the seven latent variables is larger than the suggested value of 0.5 in six cases, and six of the seven latent variables have mean variance extractions greater than the suggested value of 0.5, and one is less than 0.5 but the deviation is not large, indicating that these seven latent variables obtained a higher degree of variance from the corresponding observed variables and that the measurement model has good convergence [89]. In this study, structural equation modeling was conducted through Amos 22.0 software to investigate the overall fit evaluation and hypothesis testing of the model. In this paper, some general indicators are used to analyze the fit of the samples, resulting in the following model fit indices: normed is 2.659 (<3 is excellent), RMSEA is 0.062 (<0.08 is excellent), and CFI is 0.986 (>0.9 is excellent). All other indicators meet the criteria that the model can be adapted. It shows that this structural model meets the criteria for measurement, and the indicators test well and fit well, and the results of the path test are shown in Table 3.  
The regression coefficient of the hypothesized path is significant when the absolute value of the CR is >1.96 with \(p\) < 0.05. In accordance with this criterion, it can be seen from Table 3 that 2 out of 11 hypotheses are not valid. The test results of the model show that performance expectation has a significant positive effect on students' intention to use the digital twin learning system, and effort expectation has a significant negative effect on the intention to use. This indicates that students believe that using the digital twin learning system helps them achieve their learning goals, motivates them to use the digital twin learning system, and increases their intention to use the digital twin learning system. Meanwhile, the higher the level of effort during the use of the digital twin learning system, the lower the intention to use the digital twin learning system, which indicates that the ease of use of the learning system itself is important. The students believed that among the factors affecting the use of the digital twin learning system among students, the enabling factor and the use habit factor reflected a significant positive influence. This indicates that the compatibility of the technology itself and the supporting role it plays as part of the application of the digital twin learning system, as well as the development of students' habits of using the learning system, are essential. In addition to this, effort expectation has a significant positive effect on both hedonic motivation and performance expectation, which suggests that the ease of use of the learning system affects the students' enjoyment of using the system and improves the students' perception of the usefulness of the system. Hedonic motivation has a significant positive effect on usage habitus, which suggests that the enjoyment in using the digital twin learning system enhances students' inertia in using the system.