Mining Educational Data to Create a Model to Predict Student Retention
Mining Educational Data to Create a Model to Predict Student Retention
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Date
2015-12
Authors
McDonald, Kailey
Journal Title
Journal ISSN
Volume Title
Publisher
Middle Tennessee State University, University Honors College
Abstract
Student retention is a widespread issue in higher education. This study applies
data mining methods to analyze student data at MTSU, specifically focusing on minority
student groups. The hope of this study is to determine a set of attributes that are highly
predictive of student retention and to develop models to predict the retention status of
future students within these target groups. Decision tree classification models will be
created for each target minority group in order to predict the retention status.
We found that a student’s GPA and financial factors are the most predictive on
the student’s retention status. The developed models were able to successfully classify
students at rates as high as 68.7%. We hope that this data can help to provide the
university with a way to identify students with a high risk of failing to remain enrolled
and to improve retention rates of minority students.
Description
Keywords
data mining,
student retention rates,
minority student retention