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

Autor/inn/en	Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence
Titel	Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression
Quelle	In: Research in Higher Education, 59 (2018) 3, S.382-400 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mason, Cindi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0361-0365
DOI	10.1007/s11162-017-9473-z
Schlagwörter	Student Attrition; Engineering; Probability; Comparative Analysis; Regression (Statistics); School Holding Power; Student Characteristics; Prediction; Correlation; Case Studies + Suchen Sie Ihr Suchwort? Schülerbeurlaubung; Maschinenbau; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Regression; Regressionsanalyse; Vorhersage; Korrelation; Case study; Fallstudie; Case Study
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01