International Journal of
Information and Education Technology

Abstract

The objective of this research is to employ data mining tools and techniques on student enrollment data to predict student retention among freshman student populations. In particular, the goal is to identify freshman students who are more likely to drop out of school so that preemptive actions can be taken by the university. Through data analysis, we identify the most relevant enrollment, performance, and financial variables to construct learning models for retention prediction. The experiments have been conducted using Decision Trees, Naïve Bayes, Neural Networks, and Rule Induction models. These models have been compared and evaluated extensively. Our findings show that each model has its advantages and disadvantages and among all the input variables, students’ GPA and their financial status have bigger impact on students’ retention than other variables.