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Predicting student retention in higher education institutions (HEIs)

Letetia Addison (The University of the West Indies St Augustine Campus, Saint Augustine, Trinidad and Tobago)
Densil Williams (The University of the West Indies Five Islands Campus, Five Islands Village, Antigua and Barbuda)

Higher Education, Skills and Work-Based Learning

ISSN: 2042-3896

Article publication date: 22 August 2023

Issue publication date: 20 October 2023




This paper aims to provide a parsimonious but rigorous model to assist decision-makers to determine critical factors which can lead to higher graduation rates amongst higher education institution (HEI) participants. It predicts the odds of dropout amongst university students, using HEI data from a developing country. This is used as a basis for a Student Retention Predictive (SRP) Model to inform HEI administrators about predicted risks of attrition amongst cohorts.


A classification tool, the Logistic Regression Model, is fitted to the data set for a particular HEI in a developing country. The model is used to predict significant factors for student dropout and to create a base model for predicted risks by various student demographic variables.


To reduce dropout and to ensure higher graduation rates, the model suggests that variables such as age group, faculty, academic standing and cumulative GPA are significant. These indicative results can drive intervention strategies to improve student retention in HEIs and lessen the gap between graduates and non-graduates, with the goal of reducing socio-economic inequalities in society.


This research employs risk bands (low, medium and high) to classify students at risk of attrition or drop out. This provides invaluable insights to HEI administrators in the development of intervention strategies to reduce dropout and increase graduation rates to impact the wider public policy issue of socio-economic inequities.



The authors would like to thank the Office of the Campus Registrar and the Campus Planning Office at the University of the West Indies, St. Augustine for access to the relevant datasets used in this research.


Addison, L. and Williams, D. (2023), "Predicting student retention in higher education institutions (HEIs)", Higher Education, Skills and Work-Based Learning, Vol. 13 No. 5, pp. 865-885.



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