Autors: Andonova, L. I., Trifonov, R. I., Popov, G. I.
Title: Analysis and Prediction of Student Dropout Risk Using Machine Learning Approaches: A Case Study From Bulgarian School
Keywords: dropout prediction, education analytics, machine learning, risk factors, student retention

Abstract: School dropout is a serious educational and social problem. This study analyzes real-world data from the 68th Secondary School (2020-2025) to identify key factors associated with early school leaving - academic performance, absenteeism, disciplinary violations, and parental involvement. On this basis, a theoretical model for predicting the risk of dropping out using machine learning is proposed. The results support the need for early intervention and datadriven policymaking.

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Issue

2025 13th International Scientific Conference on Computer Science, COMSCI 2025 - Proceedings, 2025, Albania, https://doi.org/10.1109/COMSCI67172.2025.11225276

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus