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An Expanded Assessment of Data Mining Approaches for Analyzing Actuarial Student Success Rate

An Expanded Assessment of Data Mining Approaches for Analyzing Actuarial Student Success Rate

Alan Olinsky, Phyllis Schumacher, John Quinn
Copyright: © 2016 |Volume: 3 |Issue: 1 |Pages: 23
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781466693869|DOI: 10.4018/IJBAN.2016010102
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MLA

Olinsky, Alan, et al. "An Expanded Assessment of Data Mining Approaches for Analyzing Actuarial Student Success Rate." IJBAN vol.3, no.1 2016: pp.22-44. http://doi.org/10.4018/IJBAN.2016010102

APA

Olinsky, A., Schumacher, P., & Quinn, J. (2016). An Expanded Assessment of Data Mining Approaches for Analyzing Actuarial Student Success Rate. International Journal of Business Analytics (IJBAN), 3(1), 22-44. http://doi.org/10.4018/IJBAN.2016010102

Chicago

Olinsky, Alan, Phyllis Schumacher, and John Quinn. "An Expanded Assessment of Data Mining Approaches for Analyzing Actuarial Student Success Rate," International Journal of Business Analytics (IJBAN) 3, no.1: 22-44. http://doi.org/10.4018/IJBAN.2016010102

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Abstract

One way to enhance the likelihood that more university students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This paper expands upon a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained using input variables describing academic attributes of the students. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. In addition, the non-predictive method of cluster analysis is applied in order to group these students into distinct classifications based on the values of the input variables. Finally, a new approach to modeling in SAS®, called Rapid Predictive Modeler (RPM), is described and utilized. The results of the RPM also select the regression model as the best predictor.

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