# A Study on Dropout Prediction for University Students Using Machine Learning
## 2 Related Work  
Table 1 shows the existing studies related to the prediction of student dropout and compares them in terms of training data, machine learning algorithms, and prediction performance. The studies are listed in an ascending order of the dropout rate shown in their source data used for learning. [PERSON] et al. [23] used academic records collected from 7718 students at the Federal Institute of Rio Grande do Norte (IFRN) for learning, where the dropout rate was found to be 4.5%. To deal with the data imbalance, SMOTE and ADASYN were applied to the learning data. As an algorithm to develop a prediction model, Balanced bagging [24], DNN, and DT were considered, and the model implemented with DT showed the best F1-score of 0.976. One thing to note in their results is that the dropout records were classified as negative. This is contrary to the conventional approach, where the dropout records are classified as positive. If the class of the records is set to positive, their F1-score is lowered to 0.480, whose value can be calculated from the confusion matrix provided in their paper. [PERSON] et al. [8] used academic records collected from 67,060 students at Gyeongsang National University from 2015 to 2021 for their study, where the dropout rate was 5%. To deal with the data imbalance, SMOTE, SMOTE+Tomek [25], and SMOTE+ENN [25] were used. A prediction model was implemented as an ensemble of XGBoost [26] and CatBoost [27], and they obtained an F1-score of 0.808 using the model. [PERSON] [9] tried to perform the prediction using a survey of 3075 students from 2017 to 2021. To process texts in the survey documents, a Twitter morphological analyzer was used to extract words from the documents, and only nouns were used to build the data for learning. As an algorithm, Naive Bayes (NB) [28], Ridge Regression [14], DT, and RF were adopted. Among them, a model implemented with Ridge Regression showed the highest precision of 0.739. Despite the fact that their experimental data showed high skewness with a dropout rate of 6.4%, the problem of data imbalance was not discussed in their paper. [PERSON] et al. [29] used academic data collected from 331 students at the Department of Computer Science at Universidade de Tras-os-Montes e Alto Douro (UTAD) for learning, where the dropout rate was 37.5%. Random oversampling (ROS) was applied to address data imbalance. DNN, RF, and XGBoost were used to implement a model, and the RFmodel achieved the highest F1-score of 0.81. [PERSON] et al. [30] implemented a predictive model for each semester for 1418 students at a public Spanish university. As an algorithm to develop the model, Gradient Boosting (GB) [31], RF, and SVM were used. Among the models, the SVM model achieved the highest F1-score of 0.902 for students in the fourth semester, and the average F1-score for the data from all semesters was 0.804. In their experimental data, the dropout rate was 55.2%, so the performance degradation due to data imbalance was not expected to be significant. [PERSON] et al. [32] used academic data collected from 2097 students of the Escuela Politecnica Nacional leveling course from 2017 to 2018 for learning. LR and DNN were used to implement a model, and the DNN model provided the highest accuracy of 0.768. Despite the high dropout rate of 72.8% in their data, the problem of data imbalance was not discussed in their paper. [PERSON] et al. [33] used data collected from 366 students in the Department of Computer Science at Suleyman Demirel University. As an algorithm to implement a model, DNN, LR, NB, and SVM were used, and the NB model showed the highest F1-score of 0.96. Although their models achieved high accuracy, their study has a limitation in that the experimental data used for learning and validation was insufficient. In addition, the dropout rate in their data was not presented, and the issue of data imbalance was not discussed. Among the studies listed in Table 1, the proposed method can be compared with [8; 9; 29; 30; 32].