Learning from student data

Abstract

An abundance of information is contained on every college campus. Many academic, demographic, and attitudinal variables are gathered for every student who steps on campus. Despite all this information, colleges still struggle with graduation rates. This is an apt example of an overload of information but a starvation of knowledge. This paper introduces the use of neural networks and support vector machines, both nonlinear discriminant methods, for classifying student graduation behavior from several academic, demographic, and attitudinal variables maintained about students at the University of Oklahoma.