Predicting student retention in higher education institutions (HEIs)

Abstract

PurposeThis 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.Design/methodology/approachA 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.FindingsTo 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.Originality/valueThis 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.

Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students

“…Early forecasting of a student's learning success is a key tool in addressing the issue of student attrition, as such forecasts enable timely interventions to help students overcome academic challenges and achieve learning success [7,8]. The clear value of solving this task is the reason for numerous research studies and scientific articles dedicated to this issue [9][10][11].…”

WhatsApp groups: The nexus between retention, reciprocity, and user satisfaction

“…Students intention to remain at the university is measured in terms of return rates from one year to another throughout the educational journey (Addison and Williams, 2023). Keeping students enrolled and maximizing their learning experience are two driving forces in higher learning institutions (Tight, 2020).…”

Guest editorial: “New problems, new solutions”

“…Addison and Williams in their article, “ Predicting Student Retention in HEIs – A Case for Reducing Socio-economic Inequalities ” (2023) reminds us that WIL has ambitious goals to increase employability for all through widening access and increasing social mobility. They remind us of the UNSDGs and how in many developing countries, such as Trinidad and Tobago, post-Covid, there are still many inequalities, increased further by increasing crisis, and that education still faces many challenges in reducing inequalities.…”