## 1 Introduction  
Dropping out of school has globally become a significant challenge most universities are facing. According to a recent study by UNESCO [1], the disruption to education caused by COVID-19 has put 24 million learners at risk of not being able to continue their studies. The study notes that higher education is likely to experience the highest dropout rate and a projected 3.5% decline rate in enrolment, which is expected to result in 7.9 million fewer students. Many students experience various difficulties in school life, which eventually cause them to give up studying. However, the difficulties those dropout students face cannot be solved just by dropping out. After the dropout students enter society, the difficulties these dropout students face at school will turn into social pressure due to their premature entry into society and academic failure. This may result in employment difficulties, low income, and even crime [2]. The reasons for dropping out can be attributed to school, family, society, and psychology [3; 4; 5; 6; 7]. A number of studies have shown that the mono-causal approach is not enough to accurately explain the phenomenon of students dropping out, but a multitude of factors must be considered [8; 9]. The studies on online courses indicated that time-dependent data on student trajectories could be used to predict dropouts [10; 11; 12]. Dropout prediction can be transformed into a sequence classification problem by obtaining students' continuous features over time [10; 11]. However, offline education rarely uses time series to solve dropout problems because it is challenging to get the same large data streams as online education. Machine learning has been shown to effectively extract features from data [13]. Using machine learning techniques to identify students at risk of dropping out has also proven effective [14; 15; 16]. Decision tree-based models such as XGBoost [17] and random forest [18] achieve excellent results with a small number of features and sample sizes [14; 15]. The authors in [14] reported an XGBoost model with 90% accuracy in predicting dropout. In another study [15], the authors reported a 93% accuracy of the random forest model in predicting dropout. In these studies [14; 15; 16], grades proved to be the most important feature. However, a detailed discussion of the way features are generated is lacking. The impact of feature-generation methods on model performance is unclear. How the feature is generated determines the prediction model's performance [19]. The feature table represents how the feature is organized and determines the information that the model can obtain. Students in different semesters will have different data lengths; for example, a student in the third semester will have three semesters of scholarship records, while a student in the second semester will have only two. The method used to effectively process these data to generate feature tables that predict dropout has not been studied quantitatively. Most current research predicts student dropouts in the first academic year [20; 21; 22; 23; 24] and therefore avoids discussing this issue. However, the model's generalizability is limited if it only considers the first or a specific academic year. The main challenge in providing dropout predictions for students across the academic year is the length of the data. Because students in different academic years have data of different lengths, how feature tables are extracted to obtain the best predictions has not been studied quantitatively. Therefore, the purpose of this study is to explore the most applicable feature table generation methods for university dropout prediction in all academic years. In this study, based on data from 60,010 students from a Korean university, we (i) analyze dropout-related factors hidden in student trajectory data, (ii) design four sets of feature tables to summarize student trajectory data and compare the performance of six machine learning models on these feature tables.