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Norman, OK 73072

Phone: 405-325-2158
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updated: 09/29/15

Analysis of Student Data for Retention Using Data Mining Techniques

presented by

Bill Eberle, Tennessee Technological University
Doug Talbert, Tennessee Technological University

Date of webinar: September 11, 2013


At most universities, administrators and counselors are trying to devise sound methodologies to help increase student retention rates. Due to the vast amount of student data that is available, sorting through the data to extract useful knowledge is a daunting task. However, the data may be helpful in predicting future student trends particularly as it relates to retention. In this webinar, we present several data mining and machine learning techniques that can be used to predict future enrollment. In initial experiments, we applied these techniques to the retention of Computer Science students a major that historically has significant turnover in the first year of study. While traditional methods may focus on known issues with retention, we emphasize the importance of factors that may only be noticeable through the application of data mining. The goal is to determine which newly enrolled students should be targeted as retention risks. If these students can be identified early in their academic career, this approach may help increase retention rates not only within a specific department, but across an entire university.




The Consortium for Student Retention Data Exchange at the University of Oklahoma is a consortium of two-year and four-year institutions dedicated to achieving the highest levels of student success through collaboratively sharing data, knowledge, and innovation.

CSRDE is a sponsored activity of the Center for Institutional Data Exchange and Analysis at

The University of Oklahoma Outreach
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