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Sat, Nov 8, 9:00 - 9:25, Kachina B     Paper (refereed)
Recommended Citation: McAleer, B and J S Szakas.  Myth Busting: Using Data Mining to Refute Link between Transfer Students and Retention Risk.  In The Proceedings of the Information Systems Education Conference 2008, v 25 (Phoenix): §3154. ISSN: 1542-7382. (A later version appears in Information Systems Education Journal 8(19). ISSN: 1545-679X.)
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Myth Busting: Using Data Mining to Refute Link between Transfer Students and Retention Risk

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Refereed5 pages
Brenda McAleer    [a1] [a2]
The University of Maine    [u1] [u2]
Augusta, Maine, USA    [c1] [c2]

Joseph S. Szakas    [a1] [a2]
The University of Maine    [u1] [u2]
Augusta, Maine, USA    [c1] [c2]

In the past few years, universities have become much more involved in outcomes assessment. Outside of the classroom analysis of learning outcomes, an investigation is performed into the use of current data mining tools to assess the issue of student retention within the Computer Information Systems (CIS) department. Utilizing both a historical dataset of CIS students over a 10 year period, and a current student dataset, this analysis specifically deals with the following questions: 1. How can we use the past to predict retention risk of the future students? 2. Do students who transfer CIS courses (core or elective) have an increased retention risk? The data mining tool was the Oracle Data MiningTM Package used to perform tasks as classification (Na´ve Bayesian and support vector machine), and attribute importance.

Keywords: data mining, attribute importance, na´ve Bayesian model, support vector machine model, predicting retention risk, transfer students, assessment

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