Data mining for student retention management

Authors:
Shieu-Hong Lin
Affiliations:
Biola University, La Mirada, CA
Venue:
Journal of Computing Sciences in Colleges
Year:
2012

Citing 2
Cited 2

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Expert Systems with Applications: An International Journal
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Proceedings of the Fourth International Conference on Learning Analytics And Knowledge

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Abstract

We conduct a data mining project to generate predictive models for student retention management on campus. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. The project is a component in our artificial intelligence class. Students in the class get involved in the entire process of modeling and problem solving using machine learning algorithms. We examine the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machine learning algorithms are able to establish effective predictive models from the existing student retention data.