## 4 Results
### XGBOOST Result Applying Feature Selection  
The features with a high influence on modeling were selected from [PERSON]'s \(\mathbf{C}_{p}\), AIC, BIC, and adjusted \(R^{2}\) to apply to XGBOOST. The accuracy of XGBOOST for modeling by gender is shown in Table 8.  
Overall accuracy is higher when feature selection is applied than when in the \"Not Applied Feature Selection\" mode. For male students, the highest accuracy of XGBOOST is gained when a model is created using [PERSON]'s \(\mathbf{C}_{p}\) and AIC. The accuracy is 80.26%, 3.29% higher than when the BIC is applied, or no feature selection is applied.  
For female students, the highest accuracy of 81.88% is gained for XGBOOST when a model is created after applying [PERSON]'s \(\mathbf{C}_{p}\) and AIC, as in the case of male students. The accuracy was the lowest when the influencing features from the BIC were applied to XGBOOST.  
The difference in accuracy depending on the selected feature is related to the residual: The closer the mean to the sum of the residuals is to 0, the more ideally trained the model (fitted model) is, and the larger the mean to the sum of the residuals, the more inefficiently trained the model. If the selected features raised the accuracy of the prediction model, they can be interpreted as having a high influence on the predictive value.  
Figure 2 shows the important features in XGBOOST ([PERSON]'s \(\mathbf{C}_{p}\) and AIC) with the highest accuracy by gender.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gender** & **Div. (No. of Features)** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{Male} & Not Applied Feature Selection (16) & 76.97 \\  & Mallow’s \(\mathbf{C}_{p}\) (6) and AIC (6) & 80.26 \\  & BIC (3) & 76.97 \\  & Adjusted \(R^{2}\) (10) & 78.29 \\  & Not Applied Feature Selection (16) & 81.16 \\  & [PERSON]’s \(\mathbf{C}_{p}\) (6) and AIC (9) & 81.88 \\  & BIC (4) & 79.71 \\  & Adjusted \(R^{2}\) (11) & 81.56 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy of XGBOOST.  
Figure 2: XGBOOST: Important features of male and female students.  
In terms of the importance of features, regarding modeling, analysis 71 and implementation 54 had the biggest influence among other features on male students. Generalization was 19, data collection (LearningType3) was 7, and devising and proposing a solution (LearningType11) was 3. This means that the difference between the high-achieving group and the low-achieving group of male students mainly comes from the analysis and implementation of the problem. For female students, analysis (64) was the most influential; followed by implementation, generalization, and data collection (LearningType3); cooperation with friends (LearningType12); and devising and proposing a solution (LearningType11).  
The features that male and female students shared were analysis, implementation, generalization, and data collection (LearningType3), and devising and proposing a solution (LearningType11). Regardless of gender, collection of relevant data and analysis, establishing a direction for problem solving, implementation into a program, and generalization, were the features influencing modeling. Besides the common features, cooperation with friends and teaching each other (LearningType12) were revealed as important features among female students.