APPLYING MACHINE LEARNING TO PREDICT STUDENT ATTENDANCE IN VOCATIONAL EDUCATION PROGRAMS FOR LOWER SECONDARY GRADUATES (9+ SYSTEM)

Nguyen Thi Thu Trang; Tran Gia Huy; Pham Minh Chuan

Nguyen Thi Thu Trang Hung Yen University of Technology and Education
Tran Gia Huy Hung Yen University of Technology and Education
Pham Minh Chuan Hung Yen University of Technology and Education

Abstract

Attendance plays a pivotal role in determining academic performance and preventing dropout among students in Vietnam’s 9+ vocational training system. Early identification of students exhibiting low attendance is critically important for deploying effective support interventions. This study employs machine learning algorithms—including Decision Tree, Support Vector Machine (SVM), Random Forest, and Gradient Boosting—to predict attendance levels, based on learning characteristics, behavioral patterns, family background, and extracurricular activity participation. The Decision Tree model achieved optimal performance, with an accuracy of 93% when using an extended feature set. Restructuring the output labels into two classes (High–Low) contributed significantly to enhanced classification efficacy. The findings provide a foundation for implementing early warning systems to improve student management practices in vocational colleges.

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