## 4 Results and Discussion
### Model Test Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Table** & **Model Name** & **F1 Score** & **Precision** & **Recall** & **Accuracy** \\ \hline \multirow{8}{*}{Mean value-based feature table} & LightGBM & **0.79** & 0.81 & 0.78 & **0.94** \\ \cline{2-6}  & XGBoost & 0.77 & 0.76 & 0.79 & 0.93 \\ \cline{2-6}  & Logistic Regression & 0.61 & 0.50 & 0.78 & 0.80 \\ \cline{2-6}  & Support Vector Machine & 0.62 & 0.51 & 0.78 & 0.80 \\ \cline{2-6}  & Random Forest & 0.75 & 0.77 & 0.73 & 0.93 \\ \cline{2-6}  & Decision Tree & 0.66 & 0.63 & 0.70 & 0.89 \\ \cline{2-6}  & Average & 0.70 & 0.66 & 0.76 & 0.88 \\ \hline \multirow{8}{*}{Median value-based feature table} & LightGBM & 0.77 & 0.80 & 0.74 & 0.92 \\ \cline{2-6}  & XGBoost & 0.78 & **0.83** & 0.74 & 0.93 \\ \cline{2-6}  & Logistic Regression & 0.57 & 0.44 & 0.79 & 0.81 \\ \cline{2-6}  & Support Vector Machine & 0.57 & 0.44 & 0.79 & 0.81 \\ \cline{2-6}  & Random Forest & 0.75 & 0.80 & 0.71 & 0.93 \\ \cline{2-6}  & Decision Tree & 0.66 & 0.65 & 0.68 & 0.85 \\ \cline{2-6}  & Average & 0.68 & 0.66 & 0.74 & 0.88 \\ \hline \multirow{8}{*}{First-semester data-based feature table} & LightGBM & 0.68 & 0.72 & 0.65 & 0.85 \\ \cline{2-6}  & XGBoost & 0.68 & 0.75 & 0.63 & 0.87 \\ \cline{2-6}  & Logistic Regression & 0.43 & 0.29 & **0.81** & 0.81 \\ \cline{2-6}  & Support Vector Machine & 0.43 & 0.29 & **0.81** & 0.80 \\ \cline{2-6}  & Random Forest & 0.66 & 0.72 & 0.61 & 0.93 \\ \cline{2-6}  & Decision Tree & 0.55 & 0.50 & 0.60 & 0.91 \\ \cline{2-6}  & Average & 0.57 & 0.55 & 0.69 & 0.86 \\ \hline \multirow{8}{*}{Final semester data-based feature table} & LightGBM & 0.73 & 0.79 & 0.67 & 0.93 \\ \cline{2-6}  & XGBoost & 0.73 & 0.82 & 0.65 & 0.93 \\ \cline{1-1} \cline{2-6}  & Logistic Regression & 0.47 & 0.34 & 0.78 & 0.83 \\ \cline{1-1} \cline{2-6}  & Support Vector Machine & 0.47 & 0.34 & 0.78 & 0.84 \\ \cline{1-1} \cline{2-6}  & Random Forest & 0.71 & 0.79 & 0.64 & 0.92 \\ \cline{1-1} \cline{2-6}  & Decision Tree & 0.60 & 0.57 & 0.63 & 0.86 \\ \cline{1-1} \cline{2-6}  & Average & 0.62 & 0.61 & 0.69 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F1 Score, Precision, Recall, and Accuracy for class _dropout_ in the test dataset. Figure 4 presents the feature importance of the LightGBM model trained in the mean value-based feature table. The three most important features are (1) tuition fee, (2) the average number of scholarships per semester, and (3) entry year. It is evident that for Korean university students included in this study, the economic aspect may be an important factor influencing whether they drop out of school. Tuition and scholarships reflect the financial pressures burdened by students' families. This result is contrary to the study by [48], which reported that grades are the most important influencing factor. We believe this is due to the high tuition fees of private universities in South Korea, making it easier for students who cannot get scholarships to drop out. Our findings suggested that increasing scholarships and reducing tuition fees may be effective intervention measures to reduce the dropout rate. Compared to previous studies [14, 16, 21, 42, 44] on dropout prediction centered on student achievement. The findings of this study reveal the importance of features that are unrelated or not directly related to grades in predicting dropout. As shown in Figure 4, among the top ten features of feature importance, there are 6 features that are irrelevant or not directly related to grades (tuition fee, access year, birth, professional classification, absence, and living area). Since current research on dropout prediction is mainly centered on academic performance, these features may be overlooked, resulting in a portion of students being left out of the dropout prediction system. Therefore, incorporating these features that are not or directly related to grades into the dropout prediction system may improve the system's performance.