Predictive models for undergraduate student retention using machine learning algorithms

Abstract

In this paper, we have presented some results of undergraduate student retention using machine learning and wavelet decomposition algorithms for classifying the student data. We have also made some improvements to the classification algorithms such as Decision tree, Support Vector Machines (SVM), and neural networks supported by Weka software toolkit. The experiments revealed that the main factors that influence student retention in the Historically Black Colleges and Universities (HBCU) are the cumulative grade point average (GPA) and total credit hours (TCH) taken. The target functions derived from the bare minimum decision tree and SVM algorithms were further revised to create a two-layer neural network and a regression to predict the retention. These new models improved the classification accuracy. Furthermore, we utilized wavelet decomposition and achieved better results.