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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2013 Vol.3(5): 494-500 ISSN: 2010-3689
DOI: 10.7763/IJIET.2013.V3.324

Towards Freshman Retention Prediction: A Comparative Study

Admir Djulovic and Dan Li
Abstract—The objective of this research is to employ data mining tools and techniques on student enrollment data to predict student retention among freshman student populations. In particular, the goal is to identify freshman students who are more likely to drop out of school so that preemptive actions can be taken by the university. Through data analysis, we identify the most relevant enrollment, performance, and financial variables to construct learning models for retention prediction. The experiments have been conducted using Decision Trees, Naïve Bayes, Neural Networks, and Rule Induction models. These models have been compared and evaluated extensively. Our findings show that each model has its advantages and disadvantages and among all the input variables, students’ GPA and their financial status have bigger impact on students’ retention than other variables.

Index Terms—Classification, feature selection, freshman retention, prediction.

The authors are with the Computer Science Department, Eastern Washington University, Cheney, WA 99004 USA (e-mail: adjulovic@eagles.ewu.edu, danl@ewu.edu).

[PDF]

Cite:Admir Djulovic and Dan Li, "Towards Freshman Retention Prediction: A Comparative Study," International Journal of Information and Education Technology vol. 3, no. 5, pp. 494-500, 2013.

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