Mining Education Data to Predict Student's Retention: A comparative Study
This addresses student retention management for educational institutions, but it is incremental as it compares existing algorithms without introducing new methods.
The paper tackled predicting student retention in higher education using data mining, finding that some machine learning algorithms can generate effective predictive models from existing data.
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.