Abstract
A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and precision–recall (PR) curves. To that end, we trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE using the big data samples of the 165,715 high school students from the National Education Information System (NEIS) in South Korea. According to our ROC and PR curve analysis, boosted decision tree showed the optimal performance.
Highlights
The negative consequences of students’ dropping out of school are significant for both the individual and society
The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and PR curves
Given our specific goal of maximizing our chance of supporting the successful learning of all students, and minimizing the cost for intervention, the present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the SMOTE and ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) curves and precision–recall (PR) curves
Summary
The negative consequences of students’ dropping out of school are significant for both the individual and society. The society suffers losses because the nation’s productive capacity could be undermined by the shortage of the skilled workforce, and the dropout students are more likely to be frequent recipients of welfare and unemployment subsidies [2]. Because of those negative consequences, students’ dropouts have long been considered as a serious educational problem by educators, researchers, and policymakers. The students at risk are likely to drop out without carefully considering the negative consequences of their decisions or without having an opportunity to consult with experts
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