Predicting graduate student success: A comparison of neural networks and traditional techniques

The decision to accept a student into a graduate program is a difficult one. The admission decision is based upon many factors which are used to predict the success of the applicant. Regression analysis has typically been used to develop a prediction mechanism. However, as is shown in this paper, these models are not particularly effective in predicting success or failure. Therefore, this paper explores other methods of prediction, including the biologically inspired, non-parametric statistical approach of neural networks, in terms of their ability to predict academic success in an MBA program. This study found that (1) past studies may have been addressing the decision problem incorrectly, (2) predicting success and failure of graduate students is difficult given the easily obtained quantitative data describing the subjects that are typically used for such a purpose, and (3) non-parametric procedures such as neural networks perform at least as well as traditional methods and are worthy of further investigation.