A comparative analysis of machine learning techniques for student retention management
Decision Support Systems
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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This research investigates the use of Artificial Neural Networks (ANNs) to predict first year student retention rates. Based on a significant body of previous research, this work expands on previous attempts to predict student outcomes using machine-learning techniques. Using a large data set provided by Columbus State University's Information Technology department, ANNs were used to analyze incoming first-year traditional freshmen students' data over a period from 2005--2011. Using several different network designs, the students' data was analyzed, and a basic predictive network was devised. While the overall accuracy was high when including the first and second semesters worth of data, once the data set was reduced to a single semester, the overall accuracy dropped significantly. Using different network designs, more complex learning algorithms, and better training strategies, the prediction accuracy rate for a student's return to the second year approached 75% overall. Since the rate is still low, there is room for improvements, and several techniques that might increase the reliability of these networks are discussed.