# Student Dropout Prediction for University with High Precision and Recall
## 7 Conclusions  
In this paper, we present the SDP system with high precision and recall rates to accurately predict students at risk of dropping out of universities. This system is intended to assist administrators and counselors in providing personalized support to these students. The student records used in the system were asymmetrical, so various imbalanced data processing techniques were employed to determine the best algorithm for the data. Fifteen different sampling algorithms were tested with four different prediction algorithms. The SDP system employs XGBoost with SMOTE and CatBoost with RandomOverSamplerSMO-TEENN to improve the precision and recall of the predictions. The results of these two algorithms were then compared with those of four existing algorithms: logistic regression, artificial neural networks, gradient boosting, and an ensemble method. The SDP system achieved the highest scores in dropout F1. Its scores were 0.989 for accuracy, 0.819 for precision, 0.755 for recall, and 0.786 for F1. In addition, K-means clustering was used to  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Academic Year** & \begin{tabular}{c} **High-Risk** \\ **Students** \\ \end{tabular} & \begin{tabular}{c} **Low-Risk** \\ **Students** \\ \end{tabular} & \
\begin{tabular}{c} **No-Risk** \\ **Students** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ \end{tabular} \\ \hline Freshman & 143 & 181 & 6406 & 6873 \\ Sophomore & 153 & 216 & 6412 & 6938 \\ \hline Junior & 120 & 137 & 6768 & 7145 \\ \hline \end{tabular}
\end{table}
Table 16: Risk Analysis of the Students.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Reason** & **Students** & **Accuracy** & **Dropout Precision** & **Dropout Recall** & **Dropout F1** \\ \hline Employment & 180 & 0.848 & 0.238 & 0.161 & 0.192 \\ \hline Did Not Register & 540 & 0.722 & 0.569 & 0.471 & 0.516 \\ \hline Personal Issues & 514 & 0.659 & 0.405 & 0.268 & 0.323 \\ \hline
\begin{tabular}{c} Admission to \\ Other Universities \\ \end{tabular} & 608 & 0.776 & 0.672 & 0.512 & 0.581 \\ \hline \end{tabular}
\end{table}
Table 15: Dropout Student K-means Clustering Classification.  
classify the reasons for dropping out and identify the risk levels of the students, allowing administrators and counselors to provide more targeted support.  
Conceptualization, S.L.; methodology, S.K.; validation, S.K. and S.L.; formal analysis, S.K.; investigation, S.K.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, E.C. and S.L.; visualization, S.K. and S.L.; supervision, Y.-K.J., S.L.; project administration, E.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2021R1A2C1014163).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GB & Gradient Boosting \\ Ensemble & Ensemble of Gradient Boosting, Random Forest, and Support Vector Machine \\ LR & Logistic Regression \\ ANNs & Artificial Neural Networks \\ None & Unprocessed \\ Under & Undersampling \\ Over & Oversampling \\ Combine & Undersampling and Oversampling \\ SDP & Student Dropout Prediction System \\ IDP & Imbalanced Data Processor \\ DP & Dropout Predictor \\ DC & Dropout Reason Classifier \\ RSMOTEENN & RandomOverSamplerSMOTEEN \\ \end{tabular}