Research in Higher Education

, Volume 59, Issue 3, pp 382–400 | Cite as

Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

  • Cindi Mason
  • Janet Twomey
  • David Wright
  • Lawrence Whitman


As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm relationships between student attributes and attrition. Methods of prediction have also been evaluated and compared. Utilizing the attributes found in previous studies to have correlation with student attrition, this study considers the results of three different prediction methods—logistic regression, a multi-layer perceptron artificial neural network, and a probabilistic neural network (PNN)—to predict engineering student retention at a case study university. The purpose of this study was to introduce the PNN to the study of engineering student retention prediction and compare the results of the PNN to other commonly used methods in this field of study. The accuracy, sensitivity, specificity and overall results for each method are reported, compared, and discussed as the major contribution of this paper.


Student retention Probabilistic neural network Logistic regression Attrition 


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of EngineeringWichita State UniversityWichitaUSA
  2. 2.Donaghey College of Engineering and Information Technology (EIT)University of Arkansas at Little RockLittle RockUSA

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