Modeling student retention in science and engineering disciplines using neural networks

Abstract

Attracting more students into science and engineering disciplines concerned many researchers for decades. Literature used traditional statistical methods and qualitative techniques to identify factors that affect student retention up most and predict their persistence. In this paper we developed two neural network models using a feed-forward backpropagation network to predict retention for students in science and engineering fields. The first model is used to predict incoming freshmen retention and identify correlated pre-college factors. The second model is to classify freshmen groups into three classes: at-risk, intermediate, and advanced students. With total of 338 samples used, 70.1% of students classified correctly.