## 4 Research Methodology
### Measurement and Structural Pattern Analysis
#### 4.6.3 Overall Suitability of the Model  
When validating SEM theoretical models, a good model fit is a necessary condition for SEM analysis, with a better fit representing a closer match between the model matrix and the sample matrix. Several indicators of overall model fit were evaluated in this study, including \(\chi 2\) check, ratio of \(\chi 2\) to degrees of freedom, fit indicator (GFI), adjusted fit indicator (AGFI), root means squared error of approximation (RMSEA), non-standard fit indicator (NNFI), asymptotic fit indicator (IFI), comparative fit indicator (CFI), and standardized square root (SRMR). The model indicators in this study are referenced from [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] et al. (2015, 2017, 2018, 2019). In addition, three more indicators are needed in the model fitness, namely the Akira Pool Information  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**Title**} & \multicolumn{2}{c}{**Significance Estimates of Parameters**} & \multicolumn{2}{c}{**Factors**} & \multicolumn{2}{c}{**Question**} & \multicolumn{2}{c}{**Component**} & \multicolumn{1}{c}{**Convergent**} \\  & & **Unstd.** & **S.E.** & **t. Value** & \(p\) & **Std.** & **SMC** & **CR** & **AVE** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Experential \\ learning in \\ mathematics \\ \end{tabular} } & Behavioral & 1.000 & & & 0.772 & 0.596 & 0.801 & 0.574 \\  & \begin{tabular}{c} Cognitive \\ engagement \\ emotional \\ engagement \\ \end{tabular} & 0.871 & 0.089 & 9.787 & *** & 0.690 & 0.476 & & & \\  & \begin{tabular}{c} Emotional \\ engagement \\ \end{tabular} & 1.052 & 0.104 & 10.127 & *** & 0.806 & 0.650 & & \\  & \begin{tabular}{c} Mathematical \\ operations \\ \end{tabular} & 1.000 & & & & 0.668 & 0.446 & 0.773 & 0.536 \\  & \begin{tabular}{c} Spatial logic \\ Total competence \\ scores \\ \end{tabular} & 1.062 & 0.124 & 8.581 & *** & 0.641 & 0.411 & & \\  & \begin{tabular}{c} Total competence \\ scores \\ \end{tabular} & 1.832 & 0.221 & 8.275 & *** & 0.867 & 0.752 & & \\  & \begin{tabular}{c} Target confidence \\ Course competence \\ \end{tabular} & 1.000 & & & & 0.869 & 0.755 & 0.899 & 0.748 \\  & \begin{tabular}{c} Course competence \\ Course response \\ \end{tabular} & 1.213 & 0.070 & 17.251 & *** & 0.870 & 0.757 & & \\  & \begin{tabular}{c} Course response \\ sees mathematics \\ \end{tabular} & 1.135 & 0.067 & 16.982 & *** & 0.856 & 0.733 & & \\  & \begin{tabular}{c} The mathematics \\ \end{tabular} & 1.000 & & & & 0.732 & 0.536 & 0.873 & 0.633 \\  & \begin{tabular}{c} Mathematical \\ motivation \\ \end{tabular} & 1.198 & 0.092 & 13.041 & *** & 0.868 & 0.753 & & \\  & \
\begin{tabular}{c} Confidence in \\ mathematics \\ \end{tabular} & 1.270 & 0.097 & 13.081 & *** & 0.862 & 0.743 & & \\  &
\begin{tabular}{c} Mathematical values \\ \end{tabular} & 0.757 & 0.082 & 9.251 & *** & 0.708 & 0.501 & & \\ \hline \end{tabular}  
* \(p<0.05\), *
* \(p<0.01\), **
* \(p<0.001\). (Same as below).  
\end{table}
Table 1: Confidence analysis of potential conformations.  
Indicator (AIC), the Bayesian Information Indicator (BIC), and the Expected Cross Validity Indicator (ECVI). The model structure of this study is illustrated in Figure 2, and the model fitness is shown in Table 3.  
As can be seen from Table 3, for this study model, the smaller the values of the fitness indicators ECVI, AIC, BIC, and \(\chi 2\), the better, and the greater the degrees of freedom the better. The GFI, AGFI, NNFI, CFI, and IFI of the model fitness indicators are all greater than 0.9 and the CN is greater than 200. The RMSEA is in the 95% confidence interval, it does not contain 0.08, and it is less than 0.08, indicating that the RMSEA of less than 0.08 is not a coincidence; therefore, the study model has good fitting.