# A Study on Dropout Prediction for University Students Using Machine Learning
## 4 Experimental Results
### Influence of Oversampling
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm + SMOTE** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear Regression (LR) & 0.868 & 0.523 & 0.883 & 0.657 \\ Decision Tree (DT) & 0.929 & 0.706 & 0.868 & 0.778 \\ Random Forest (RF) & 0.949 & 0.797 & 0.866 & 0.830 \\ Support Vector Machine (SVM) & 0.921 & 0.668 & 0.890 & 0.763 \\ Deep Neural Network (DNN) & 0.928 & 0.691 & 0.904 & 0.784 \\ Light Gradient Boosting Machine (LightGBM) & - & - & - & - \\ \hline Mean & 0.919 & 0.677 & 0.882 & 0.762 \\ Standard Deviation & 0.031 & 0.099 & 0.016 & 0.064 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of the proposed prediction models after applying SMOTE. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm + ADASYN** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear Regression (LR) & 0.841 & 0.472 & 0.920 & 0.624 \\ Decision Tree (DT) & 0.919 & 0.663 & 0.880 & 0.756 \\ Random Forest (RF) & 0.947 & 0.778 & 0.880 & 0.826 \\ Support Vector Machine (SVM) & 0.900 & 0.602 & 0.890 & 0.718 \\ Deep Neural Network (DNN) & 0.910 & 0.625 & 0.932 & 0.749 \\ Light Gradient Boosting Machine (LightGBM) & - & - & - & - \\ \hline \hline Mean & 0.903 & 0.628 & 0.901 & 0.735 \\ Standard Deviation & 0.039 & 0.111 & 0.024 & 0.073 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance of the proposed prediction models after applying ADASYN. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm + ADASYN** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear Regression (LR) & 0.840 & 0.470 & 0.913 & 0.621 \\ Decision Tree (DT) & 0.920 & 0.668 & 0.885 & 0.761 \\ Random Forest (RF) & 0.948 & 0.790 & 0.871 & 0.829 \\ Support Vector Machine (SVM) & 0.912 & 0.640 & 0.885 & 0.743 \\ Deep Neural Network (DNN) & 0.915 & 0.642 & 0.925 & 0.758 \\ Light Gradient Boosting Machine (LightGBM) & - & - & - & - \\ \hline Mean & 0.907 & 0.642 & 0.896 & 0.742 \\ Standard Deviation & 0.040 & 0.114 & 0.022 & 0.076 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance of the proposed prediction models after applying Borderline-SMOTE. The performance degradation after applying oversampling was also discussed in the previous studies of [39,40]. The reason can be explained by the fact that the new dropout records of the P class augmented by SMOTE or other techniques acted as noise when the classification process was performed. As shown in Figure 4, as the overlapping region between the P and N classes increases, classification becomes more difficult. To see how much portion of our source data overlaps, we compared the data distribution of dropout and non-dropout students. Among the 10 attributes used for training, we examined the distribution of grades and the number of leaves of absence, which had a high correlation with student dropout. Figure 6 shows the distribution of the two attribute values for students in the Computer Science department of Sahmyook University, where purple circles denote the values of dropout students while yellow circles denote the values of non-dropout students. As shown in the figure, the data of the two groups significantly overlapped in the areas where the grade ranges from 1.0 to 4.0, and the number of absences ranges from 1 to 4 semesters. In this case, if dropout records are oversampled and added to the P class, the number of FP errors might increase significantly, which can subsequently decrease the precision of the prediction models.