# A Study on Dropout Prediction for University Students Using Machine Learning
###### Abstract  
Student dropout is a serious issue in that it not only affects the individual students who drop out but also has negative impacts on the former university, family, and society together. To resolve this, various attempts have been made to predict student dropout using machine learning. This paper presents a model to predict student dropout at Sahmyook University using machine learning. Academic records collected from 20,050 students of the university were analyzed and used for learning. Various machine learning algorithms were used to implement the model, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Deep Neural Network, and LightGBM (Light Gradient Boosting Machine), and their performances were compared through experiments. We also discuss the influence of oversampling used to resolve data imbalance issues in the dropout data. For this purpose, various oversampling algorithms such as SMOTE, ADASYN, and Borderline-SMOTE were tested. Our experimental results showed that the proposed model implemented using LightGBM provided the best performance with an F1-score of 0.840, which is higher than the results of previous studies discussing the dropout prediction with the issue of class imbalance.  
dropout prediction; machine learning; class imbalance; oversampling; LightGBM +
Footnote †: journal: _Sci_  
002320032003203200320  
to 80-95% [11; 12; 13]. From this, the prediction accuracy can be significantly influenced by the data imbalance caused by the extremely high or low dropout rate. In order to achieve high accuracy, it is essential to reflect such imbalance properly in learning. It is also necessary to adopt performance indicators that account for the data imbalance when evaluating the performance of prediction models.  
This paper presents a model to predict student dropout at Sahmyook University, a small- and medium-sized 4-year university located in Seoul. About 168,000 academic records collected from 20,050 students from 2010 to 2022 were analyzed and used for learning. Various machine learning algorithms were used to implement the model, including Logistic Regression (LR) [14], Decision Tree (DT) [15], Random Forest (RF) [16], Support Vector Machine (SVM) [17], Deep Neural Network (DNN) [18], and Light Gradient Boosting Machine (LightGBM) [19], and their performance were compared through experiments. In addition, we discuss the influence of oversampling used to resolve the data imbalance mentioned above. For this purpose, various oversampling techniques, such as SMOTE [20], ADASYN [21], and Borderline-SMOTE [22], were tested. To compare the performance of models, the F1-score was used, which is a performance metric that properly reflects the data imbalance in the performance evaluation.  
This paper is organized as follows. Section 2 introduces existing studies related to dropout prediction using machine learning. Section 3 describes the structure of source data, feature selection, and model implementation using machine learning algorithms. Section 4 provides experimental results that compare the performance of the prediction models and show the influence of oversampling on the prediction performance. Section 5 concludes the paper with the future research directions.