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

Author

Listed:
  • Cindi Mason

    (Wichita State University)

  • Janet Twomey

    (Wichita State University)

  • David Wright

    (Wichita State University)

  • Lawrence Whitman

    (University of Arkansas at Little Rock)

Abstract

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.

Suggested Citation

  • Cindi Mason & Janet Twomey & David Wright & Lawrence Whitman, 2018. "Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(3), pages 382-400, May.
    • Handle: RePEc:spr:reihed:v:59:y:2018:i:3:d:10.1007_s11162-017-9473-z
    DOI: 10.1007/s11162-017-9473-z
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    References listed on IDEAS

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    1. J. -P. Vandamme & N. Meskens & J. -F. Superby, 2007. "Predicting Academic Performance by Data Mining Methods," Education Economics, Taylor & Francis Journals, vol. 15(4), pages 405-419.
    2. Robst, John & Keil, Jack & Russo, Dean, 1998. "The effect of gender composition of faculty on student retention," Economics of Education Review, Elsevier, vol. 17(4), pages 429-439, October.
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    Cited by:

    1. Iván Sandoval-Palis & David Naranjo & Jack Vidal & Raquel Gilar-Corbi, 2020. "Early Dropout Prediction Model: A Case Study of University Leveling Course Students," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    2. Kubitza, Dennis Oliver & Weßling, Katarina, 2025. "Whole Lotta Training - Studying School-to-Training Transitions by Training Artificial Neural Networks," EconStor Preprints 310974, ZBW - Leibniz Information Centre for Economics.

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