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ERIC Number: EJ1076300
Record Type: Journal
Publication Date: 2015-Feb
Pages: 29
Abstractor: As Provided
Reference Count: 66
ISBN: N/A
ISSN: ISSN-1521-0251
Exploring Student Characteristics of Retention That Lead to Graduation in Higher Education Using Data Mining Models
Raju, Dheeraj; Schumacker, Randall
Journal of College Student Retention: Research, Theory & Practice, v16 n4 p563-591 Feb 2015
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.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A