## 4 Methods  
### Data  
**Samples**. Our sample consists of the big data samples of 165,715 high school students from the NEIS database of 2014. Those are students from two big cities and two provinces in South Korea: Seoul, Incheon, Gyeongsangbuk-do, and Gyeongsangnam-do. The proportion of male students is 0.60. The proportions of freshman, sophomore, and senior in high school are 0.33, 0.34, and 0.33, respectively. The NEIS is the web-based integrated administration system that connects South Korean's education organizations including around 12,000 elementary, middle and high schools, 17 city and provincial offices of education, and the Ministry of Education. The NEIS was developed by South Korea's Ministry of Education in the early 2000s for several purposes. The NEIS was designed to reduce teachers' workload. For example, teachers do not need to prepare various reports, statistics, and administrative documents. In addition, the NIES was designed to enhance the conveniences of citizens, especially parents. For example, parents can request 38 types of student information (e.g., school schedule, meal schedule, grades, and absence) and official certificates online. The NEIS was also designed to enhance the efficiency in school administration. For example, the NEIS reduces manual document preparation, enhances information sharing, and improves decision making processes at a policy level. Currently, the NEIS is maintained by the Korea Education & Research Information Service (KERIS) under the Ministry of Education. The NEIS has two databases. The educational affairs database contains information more than six million students, such as academic achievement, absence, health and so on. The school administration database contains information about HR affairs, teacher information, and school information. Ethics Committee/Institutional Review Board approval for this study was not sought because we used the government data that are already collected in the NEIS system, and IRB approval is not required in South Korea in this case.  
**Target label**. In this study, the target label for prediction is students' dropouts. The binary target label representing students' dropouts was created based on variables named \"the school register change\" and \"the reasons for the school register change\" in the NEIS database. The variable named \"the school register change\" has 15 categories such as entrance, expulsion, leaving, transfer, and graduation. The variable named \"the reasons for the school register change\" describes 43 reasons for the changes. As discussed above, we are interested in the dropouts among those at-risk youths who could benefit from the intervention programs informed by the dropout early warning system. Therefore, in this study, we defined dropout students as the ones who have dropped out of school for the 13 negative reasons presented in Table 4. In total, out of 165,715 students, 1348 students (0.81%) were identified as dropout students in our analysis.  
**Features.** Following the recommendation from the National High School Center, we used the attendance, behavior, and course performance (the \"ABCs\") as the key indicators for our dropout predictions [32]. Previous studies also showed that low attendance at the beginning of a semester can be an indicator for the dropout prediction [33]. Therefore, we further included the attendance in the first four weeks as indicators. In sum, we used 15 features to predict students' dropouts: the unauthorized absence in the first four weeks, unauthorized early leave in first four weeks, unauthorized class absence in first four weeks, unauthorized lateness in first four weeks, unauthorized absence, unauthorized early leave, unauthorized class absence, unauthorized lateness, number of self-regulated activities, number of club activities, number of volunteer activities, number of career development activities, normalized ranking on Korean, normalized ranking on Math, and normalized ranking on English.