Analysis of Student Dropout Risk in Higher Education Using Proportional Hazards Model and Based on Entry Characteristics

Liga Paura (), Irina Arhipova, Gatis Vitols and Sandra Sproge
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Liga Paura: Institute of Computer Systems and Data Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
Irina Arhipova: Institute of Computer Systems and Data Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
Gatis Vitols: Institute of Computer Systems and Data Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
Sandra Sproge: Study Centre, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia

Data, 2025, vol. 10, issue 7, 1-18

Abstract: The aim of this study is to identify the key factors contributing to student dropout and to develop a predictive model that estimates the dropout risk of students based on their entry characteristics and enrolment registration data. Our analysis is based on the registration and academic data of 971 full-time and part-time bachelor’s students in five faculties, who were enrolled in the academic year 2021–2022 at the Latvia University of Life Sciences and Technologies (LBTU). The dropout analysis was done during the 3.5 years of study, when the students started their last semester in engineering and information technology, agriculture and food technology, economics and social sciences, and forest and environmental studies and when veterinary medicine students had completed more than half of their program of study. Survival analysis methods were used during the study. Students’ dropout risk in relation to gender, faculty, priority to study in the program, and secondary school performance (SM) was estimated using the Proportional hazard model (Cox model). The highest student dropout was observed during the first year of study. Secondary school performance was a significant predictor of students’ dropout risk; students with higher SM had a lower dropout risk (HR = 0.66, p < 0.05). As well, student dropout can be explained by faculty or study programme. Students in economics and social sciences were at lower dropout risk than the students from the other faculties. Results show the model’s concordance index was 0.59, and this indicates that additional or stronger predictors may be needed to improve model performance.

Keywords: dropout; higher education; life sciences; engineering sciences; veterinary medicine (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:7:p:110-:d:1697292

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