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

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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)

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.

Suggested Citation

  • Liga Paura & Irina Arhipova & Gatis Vitols & Sandra Sproge, 2025. "Analysis of Student Dropout Risk in Higher Education Using Proportional Hazards Model and Based on Entry Characteristics," Data, MDPI, vol. 10(7), pages 1-18, July.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:7:p:110-:d:1697292
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    References listed on IDEAS

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    1. Astorne-Figari, Carmen & Speer, Jamin D., 2018. "Drop out, switch majors, or persist? The contrasting gender gaps," Economics Letters, Elsevier, vol. 164(C), pages 82-85.
    2. Diogo E. Moreira da Silva & Eduardo J. Solteiro Pires & Arsénio Reis & Paulo B. de Moura Oliveira & João Barroso, 2022. "Forecasting Students Dropout: A UTAD University Study," Future Internet, MDPI, vol. 14(3), pages 1-14, February.
    3. Dimitrios Kalamaras & Laura Maska & Fani Nasika, 2025. "A Cox Proportional Hazards Model with Latent Covariates Reflecting Students’ Preparation, Motives, and Expectations for the Analysis of Time to Degree," Stats, MDPI, vol. 8(2), pages 1-13, May.
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