# Student Dropout Prediction for University with High Precision and Recall
## 2 Related Work
In the 3 rd semester model, both the dropout recall and dropout precision were the highest in the random forest model, at 88.462 and 86.792, respectively. In the 4 th semester model, the dropout recall and dropout precision were the highest in the support vector machine model, at 91.549 and 89.041, respectively. Barros et al. [3] gathered 7718 student records from the Federal Institute of Rio Grande do Norte, utilizing 6 mathematics and 19 demographics- and socio-economic-related courses as feature sets. To deal with imbalanced data, they employed downsampling, SMOTE, ADYSYN, and balanced bagging techniques for each experiment. They tested artificial neural network and decision tree models with training/test ratios of 75% and 25%, respectively. The highest precision was obtained using the oversampling (SMOTE, ADAYSYN) technique of artificial neural networks at 0.991. For recall and F1, the unprocessed decision tree method performed the best, at 0.977 and 0.976, respectively. Baranyi et al. [4] not only acquired the university transcript and personal information but also utilized high school grades to predict dropouts. They used balanced 8319 students from 2013 to 2019 at the Budapest University of Technology and Economics, composing 30 feature sets--5 related to the university program, 21 to high school, and 4 to personal data. They tested various models, such as artificial neural networks, Tabnet, XGBoost, random forest, and BaggingFCNN. They optimized the hyperparameters of artificial neural networks using the hyperas package. The authors also used SHAP analysis to identify the most influential variables and found that \"years elapsed\" (the years since the mature examination) was the most influential variable, followed by grade-related features such as \"University admission score\". The experiment showed that artificial neural networks had the highest precision of 0.747 and recall of 0.667. Niyogisubizo et al. (2016) predicted class dropout using data from Constantine the Philosopher University in Nitra from 2016 to 2020. The authors utilized primary data, including \"tests\", \"access\", and \"project\", which had a high correlation. They stacked random forest, XGBoost, and gradient boosting and used the output as input for artificial neural networks. The stacking ensemble showed high performance with overall precision, recall, and F1 score values of 0.93, 0.93, and 0.92, respectively, of midpoint and midpoint deviation. The review of related work found that many previous studies had small data sets and did not address imbalanced data. Additionally, most of the works considered academic grades as the most important predictor of student dropout. However, the methods and data sets used in these studies varied, making it challenging to compare the models. Furthermore, the results of previous works often only showed high precision or recall metrics but not both, and the reasons for dropout were not analyzed. To address these gaps, the authors of this study used a large data set spanning five years and included student activities in addition to academic grades. We also compared the proposed approach with existing models using our data to ensure a fair comparison. We used a hybrid model to achieve high precision and recall rates for predicting student dropout. Finally, we analyzed the reasons for dropout to assist counselors and administrators in supporting students and making informed decisions. Overall, this study contributes to the field by using a comprehensive approach that considers various factors to predict student dropout and analyzes the underlying reasons for dropout.