Literaturnachweis - Detailanzeige
Autor/inn/en | Raju, Dheeraj; Schumacker, Randall |
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Titel | Exploring Student Characteristics of Retention That Lead to Graduation in Higher Education Using Data Mining Models |
Quelle | In: Journal of College Student Retention: Research, Theory & Practice, 16 (2015) 4, S.563-591 (29 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 1521-0251 |
Schlagwörter | Student Characteristics; Higher Education; Graduation Rate; Academic Persistence; Models; Student Records; Regression (Statistics); Data Analysis; Data Collection; Data Interpretation; College Freshmen; Predictor Variables; Grade Point Average; Credits; Mathematical Models |
Abstract | 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. (As Provided). |
Anmerkungen | 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 |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |