## 2 Related Works
### Student Dropout Prediction  
The key factors associated with student dropouts have been described in the previous section; however, the effective use of these features for dropout prediction requires machine learning techniques. The literature has revealed that the pattern hidden in educational data can be used to predict student dropout using machine learning technologies and that better predictive models can be developed by combining knowledge from other fields [31]. [PERSON] et al. [32] improved the traditional decision tree algorithm using Renyi Entropy, Information Gain, and Association Function. The authors reported that the accuracy of predicting student dropout was 97.50%, significantly higher than the traditional decision tree's 92.50%. The authors in [33] proposed the Bayesian profile regression approach and emphasized the importance of students' performance, motivation, and resilience in identifying students at risk of academic failure.  
The key factors determining model performance are features and sample size. Table 1 sums up the models, sample sizes, features, metrics used and the results obtained in previous studies.  
High school grades and performance are often used to predict dropout probability for first-year college freshmen. [PERSON] and [PERSON] [24] used personal information and high school grades to predict the dropout probability for first-academic-year freshmen. The study used a large dataset containing 15,825 student data. However, the insufficient accuracy (0.74) indicates that the feature or model needs improvement. [PERSON] and [PERSON] [34] used high school data and academic grades as features and reported an accuracy of 0.78. This result shows that adding academic grade information can improve the accuracy of the model's prediction. [PERSON] et al. [16] also demonstrated this result with a larger dataset. [PERSON] [22] used academic performance to predict first-year student dropout based on 5955 students' data. The author claimed that accuracy is directly related to dataset size. Other studies reveal that better predictive models can be developed by combining knowledge from other features. [PERSON] et al. [35] reported a 0.89 accuracy of the decision tree based on 3176 students' data. The authors found a strong correlation between the average exam pass rate and dropout, which allows us to reduce the model complexity and get good results. [PERSON] and [PERSON] [15] proposed a more fine-grained model based on course-level features. Although the dataset used is very small, the dropout prediction model tailored to individuals may be more accurate. [PERSON] et al. [14] found that students' personal details, like age, are also an important factor in student dropout.  
However, the complexity of dropout prediction is not only reflected in the selection of features but also in the processing of time-dependent features. As stated in the fourth paragraph of Section 1, there have been no quantitative studies on how to handle student data of different lengths in different semesters. Some studies [14; 34; 35] do not use features that vary across academic years or do not discuss how data from different years are treated. Other studies [15; 16] used data from the first academic year or a specific academic year to predict dropout and therefore do not discuss this issue. In similar areas of research, for example, predicting online course dropout, the impact of time-dependent features on dropout has been identified [10; 11; 36]. However, dropout prediction methods based on online courses are not suitable for offline dropout prediction due to different data structures. Therefore, it is necessary to quantitatively study the processing methods of time-related features.