## 4 Result
### Feature Importance Analysis Using XGBoost and Comparative Review with SHAP
#### 4.2.1 Feature Importance Analysis for AI Literacy in the Class Preparation Phase  
Feature Importance Analysis for AI Literacy in the Class Preparation Phase is detailed in [Table 6, Figure 4]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**SHAP**} \\  & **Feature Importance** & **Mean Absolute Value** \\ \hline Importance of AI Literacy in Class Preparation Phase & 0.2488 & 0.1978 \\ \hline Importance of AI Literacy in Class Application of Assessment & & & \\ Results Phase & 0.1208 & 0.0679 \\ \hline Importance of Teaching Methods in AI Literacy for & & & \\ AI-Integrated Education & 0.1109 & 0.0748 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Feature Importance of AI Literacy in the Class Preparation Phase. The background color is used to highlight cases with higher values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **R\({}^{2}\) Score** & **MSE** & **RMSE** & **MAE** \\ \hline Self-Perceived Level of AI Literacy for Class & & & & \\ Preparation Phase execution & 0.8206 & 0.1235 & 0.3514 & 0.1430 \\ \hline Self-Perceived Level of AI Literacy for Class & & & & \\ Implementation Phase execution & 0.8007 & 0.1337 & 0.3656 & 0.1372 \\ \hline Self-Perceived Level of AI Literacy for Class & & & & \\ Assessment Phase execution & 0.8066 & 0.1534 & 0.3917 & 0.1561 \\ \hline Self-Perceived Level of AI Literacy for Class & & & & \\ Application of Assessment Results Phase execution & 0.7746 & 0.1616 & 0.4019 & 0.1581 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation of the XGBoost model on target variables. The regression analysis with XGBoost recorded the highest Feature Importance value for the \"Importance of AI Literacy in Class Preparation Phase\" feature (0.2488). This was followed by \"Importance of AI Literacy in Class Utilizing Assessment Results Phase\" (0.1208), \"Importance of Teaching Methods in AI Literacy for AI-Integrated Education\" (0.1109), \"Importance of AI Literacy in Class Assessment Phase\" (0.1009), and \"Importance of AI Literacy in Class Implementation Phase\" (0.0650). Comparative analysis with SHAP indi \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & \
\begin{tabular}{c} **XGBoost** \\ **Feature Importance** \\ \end{tabular} &
\begin{tabular}{c} **SHAP** \\ **Mean Absolute Value** \\ \end{tabular} \\ \hline Importance of AI Literacy in Class Assessment Phase & 0.1009 & 0.0771 \\ \hline Importance of AI Literacy in Class Implementation Phase & 0.0650 & 0.0521 \\ \hline Importance of Skills in AI Literacy for AI-Integrated Education & 0.0610 & 0.0918 \\ \hline Perceived Need for AI Literacy Learning Level for Students & 0.0559 & 0.0723 \\ \hline Importance of Skills in AI Literacy & 0.0547 & 0.0837 \\ \hline Importance of Knowledge in AI Literacy for & 0.0544 & 0.0489 \\ \hline Importance of Knowledge in AI Literacy & 0.0465 & 0.0546 \\ \hline Perceived Need for AI Literacy Learning Level for Teachers & 0.0406 & 0.0543 \\ \hline Importance of Teaching Methods in AI Literacy & 0.0404 & 0.0437 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 4: Feature Importance Comparison: XGBoost and SHAP Analysis for AI Literacy in the Class Preparation Phase. cated that some features, such as \"Importance of AI Literacy in Class Utilizing Assessment Results Phase\" (XGBoost: 0.1208, SHAP: 0.0679) and \"Importance of AI Literacy in Class Implementation Phase\" (XGBoost: 0.0650, SHAP: 0.0521), showed lower mean absolute values in SHAP than their XGBoost counterparts. Conversely, features like \"Importance of Skills in AI Literacy for AI-Integrated Education\" (XGBoost: 0.0610, SHAP: 0.0918) and \"Importance of Skills in AI Literacy\" (XGBoost: 0.0547, SHAP: 0.0837) displayed higher mean absolute values in SHAP analysis, suggesting discrepancies in how the models assess feature contributions. The SHAP findings provide further insight into these discrepancies by elucidating why certain variables carry higher or lower importance.