Exploring student characteristics of retention that lead to graduation in higher education using data mining models

Academic Article

Abstract

  • © 2015 SAGE Publications. The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree and logistic regression models indicated first semester GPA, earned credit hours after end of first semester, status (full/part time) at the end of semester, and high school GPA as the most important variables. Of the 22,099 students who were full-time, first time freshmen from 1995-2005, 7,293 did not graduate (33%). Out of the 7,293 who did not graduate, 2,845 students (39%) had first semester GPA < 2.25 with less than 12 earned credit hours. Characteristics of student retention leading to graduation can be predicted as early as end first semester instead of waiting until the end of the first year of school.
  • Digital Object Identifier (doi)

    Author List

  • Raju D; Schumacker R
  • Start Page

  • 563
  • End Page

  • 591
  • Volume

  • 16
  • Issue

  • 4