Mining Education Data to Predict Student's Retention: A comparative Study

TL;DR

This study compares various machine learning algorithms to develop predictive models for student retention, aiming to identify students needing support and improve higher education quality.

Contribution

It evaluates and compares the effectiveness of different machine learning algorithms in predicting student retention from educational data.

Findings

Some algorithms produce highly accurate retention predictions

Predictive models can identify students at risk of dropout

Machine learning enhances retention management strategies

Abstract

The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.