# Student Dropout Prediction for University with High Precision and Recall
## 2 Related Work  
Yaacob et al. [5] conducted a study on 64 computer science students in the 1 st and 2 nd semesters in the year 2016, measuring their academic grades in 26 courses including mathematics and IT courses. The authors experimented with several machine learning models, such as logistic regression, KNN, random forest, artificial neural networks, and decision trees, to predict the students' performance. Although the data were imbalanced, no special imbalanced data processing was applied. Logistic regression exhibited the highest accuracy and AUC values. However, the authors did not measure the dropout precision and dropout recall metrics; instead, they evaluated their model's performance using the AUC. Shynarbek et al. [6] collected 366 student records in the department of computer science at Suleyman Demirel University, comprising grades in mathematics and computer-related courses from 2016 to 2017. The authors created a feature set using only mathematics and computer subjects and applied several machine learning models, such as the naive Bayes model, support vector machines, logistic regression, and artificial neural networks, to predict students' academic performance. Unlike Yaacob et al. [5], they used four metrics, accuracy, recall, precision, and F1 score, to measure the prediction rate. Shynarbek et al. [6] replaced the missing values with random values in the data preparation process. The data imbalance was not mentioned in the paper. The naive Bayes and artificial neural network methods exhibited the highest accuracy (0.96) and precision (0.94), recall (0.94), and F1 (0.94) scores, respectively. Silva et al. [7] used 331 undergraduate students' academic grades and personal information, including 23 feature sets, from the department of computer engineering at Universidade de Tras-os-Montes e Alto Douro (UTAD) from 2011 to 2019. Of the 331 students, 124 are dropouts and 207 are students who successfully graduated from the university. The authors applied several machine learning models, such as CatBoost, random forest, XGBoost, and artificial neural networks, to predict the students' academic performance. To handle imbalanced data, they applied RandomOverSampling during preprocessing. In the preprocessing process, they scaled the data with MinMaxScaler and performed RandomOverSampling as imbalanced data processing. The authors used three metrics, precision, recall, and F1 score, to evaluate the models' performance. The training/test ratio was 8:2, and they performed 10-fold cross-validation. Artificial neural networks, XGBoost, and random forest exhibited the highest precision (0.85), recall (0.83), and F1 score (0.81), respectively. Fernandez et al. [8] collected data from 1418 undergraduate students, where 783 were dropouts and 635 were non-dropouts. The feature set comprised 19 enrollment-related fields, 14 qualification-related fields, and 4 scholarship-related fields, excluding student IDs and redundant data. The authors used numerical data with MinMaxScaler and categorical data with one-hot encoding. To handle the imbalance in the data, the authors applied the SMOTETomek method, a combination of the SMOTE and Tomek links methods, during preprocessing. The authors applied several machine learning models, such as gradient noosting, random forest, support vector machine, and ensemble models, to predict the students' dropout rate in each semester. They evaluated the models' performance using the dropout recall and dropout precision metrics. In the enrollment model, the dropout recall of gradient boosting was the highest at 72.340, and dropout precision using the support vector machine method was the highest at 65.854. In the 1 st semester model, the dropout recall was 82.237 for the ensemble model and gradient boosting had the highest dropout precision of 84.277. In the 2 nd semester model, the ensemble model had the highest dropout recall at 82.237, and the gradient boosting had the highest dropout precision at 79.245.