# Student Dropout Prediction for University with High Precision and Recall
## 1 Introduction  
According to South Korea's 2022 Basic Education Statistics [1], the school-age population is declining. Compared to 2021, the 2022 school-age population of South Korean universities decreased by 2.6% and that of colleges decreased by 6.4%. The number ofstudents matriculating to universities has significantly decreased since the advent of the COVID-19 pandemic. The decline in the school-age population is a severe problem in South Korea. When the school-age population declines, universities must be prepared to both recruit new students and prevent current ones from dropping out. One of the main concerns for universities is the student dropout rate, which not only costs individuals but also impacts universities, local communities, and the nation. For instance, in 2020 the dropout rate increased by 1.1%, with rates of 1.9% in metropolitan areas and 3% in provinces. Students in provincial universities may be more susceptible to dropping out due to factors such as employment prospects or career goals [2]. Those who drop out often transfer to higher-ranked universities in metropolitan areas. This trend is expected to exacerbate the already significant issue of population influx to urban areas, leading to a structural waste of educational and financial resources. Furthermore, the problem has additional side effects, including missed educational opportunities for those who could have enrolled, disruptions to the academic atmosphere, and a waste of management resources. Universities can prevent students from wasting time and losing interest by predicting which students are likely to drop out and providing personalized support. In order to do so, universities require a system that can be responsive to students who experience a change of heart. Several studies [3; 4; 5; 6; 7; 8] have examined the prediction of student dropout rates in universities. Two key factors crucial in predicting dropout rates are precision and recall. High dropout precision means that the model can accurately predict which students are likely to drop out, which is essential for directing counseling resources. Conversely, high dropout recall is important because it enables universities to identify all students who are at risk of dropping out. When only one of the indexes is high, there can be problems. For instance, a model may correctly predict twenty students who are about to drop out (100% precision) but fail to predict another forty (50% recall), resulting in missed counseling opportunities. Alternatively, a model may correctly predict all students who will drop out (100% recall) but have low precision, wasting university resources on students who will not actually drop out. In this paper, we solve the problem of predicting the students who are about to drop out of the university using data-driven algorithms. The advantages of data-driven algorithms are their abilities to automatically learn from data, adapt to changing circumstances, and improve their performance over time. They uncover patterns and insights that may not be immediately apparent to human analysts and process large amounts of data quickly and accurately. Improving precision and recall indexes depends on the quantity and quality of data accumulated by the university. Since universities typically maintain various student records, data quality is generally not a significant concern. However, ensuring an adequate amount of data that describe students who drop out is essential for predictive models' accuracy. Dealing with asymmetrical data is challenging because the average student dropout rate is low, at only 1.9% in metropolitan areas and 3% in provinces. Consequently, over 97% of the data do not describe students who drop out. To overcome this challenge, the preprocessing of the feature set is necessary to address imbalanced data and prevent them from negatively affecting machine learning processes. The imbalanced data preprocessing methods can be categorized into an algorithmic approach, a data approach, and a cost-sensitive approach [9].