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Use Of A Neural Network Model And Noncognitive Measures To Predict Student Matriculation In Engineering

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2007 Annual Conference & Exposition


Honolulu, Hawaii

Publication Date

June 24, 2007

Start Date

June 24, 2007

End Date

June 27, 2007



Conference Session

Cognitive and Motivational Issues in Student Performance II

Tagged Division

Educational Research and Methods

Page Count


Page Numbers

12.1522.1 - 12.1522.11



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Paper Authors


P.K. Imbrie Purdue University

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P.K. Imbrie is an Associate Professor of Engineering in the Department of Engineering Education at Purdue University. He holds B.S., M.S. and Ph.D. degrees in Aerospace Engineering from Texas A&M University. His educational research interests include: assessment of student learning, modeling of student success, modeling of student team effectiveness, and technology enabled learning. His technical research interests include: solid mechanics; experimental mechanics; microstructural evaluation of materials; nonlinear materials characterization, microstructural evaluation of materials, piezospectroscopic techniques, and experiment instrument design.

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Joe Jien-Jou Lin

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Joe J.J. Lin is a doctoral student in the Department of Engineering Education at Purdue University. He received his M.S. degree in Industrial Engineering from Purdue University and B.S.I.E. from National Tsing-Hua University in Taiwan. He worked as a production control engineer before joining Purdue. His research interests include student success models, team effectiveness, neural network, fuzzy computing, data mining and production systems.

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Tayo Oladunni Purdue University, West Lafayette

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Olutayo O. Oladunni is a Post-Doctoral Research Assistant in the Dept. of Engineering Education at Purdue University. He received his B.S. in Systems Engineering and Management from Richmond College, London, earned his M.S. (2002) and Ph.D. (2006) in Industrial Engineering from the University of Oklahoma, Norman, Oklahoma.

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Kenneth Reid

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Ken Reid is currently pursuing a Ph.D. in Engineering Education at Purdue University as well as an Associate Professor in Electrical and Computer Engineering Technology at IUPUI. He has a BS in Computer and Electrical Engineering from Purdue University, and an MSEE from Rose-Hulman Institute of Technology. He is working to assess success in first year engineering and engineering technology students.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Use of a Neural Network Model and Noncognitive Measures to Predict Student Matriculation in Engineering

Abstract Engineering students’ affective self-beliefs prior to their first year have the potential to help researchers better understand various issues related to student retention and engagement. This paper examines whether a neural network model based on student noncognitive characteristics can be used to predict student persistence in engineering, and the influence of gender in the predictive model. Eight noncognitive measures (i.e., academic self-efficacy, academic motivation, leadership, metacognition, career, type of learner (e.g., deep vs. surface), teamwork, and expectancy-value) serve as independent parameters to an artificial neural network (NN) that is used to predict student persistence within engineering school at the end of first year. A feed-forward neural network model with back-propagation training was developed to predict third semester retention of a cohort of first-year engineering students (N=1,523) at a large Midwestern university. The model constituted of 159 primary nodes corresponding to 8 noncognitive factors described by a 159 item instrument. The resulting model was shown to have a predicative accuracy of 82% for retained students after their first year and 30% for non-retained students. Significantly decreasing the number of inputs (i.e., only using those items that appeared to have the strongest influence) had little impact on the predicative accuracy of the retained students. However, the reduction in inputs decreased the predictive accuracy of the non-retained students by approximately 10%. Results for the same cohort also indicate that the neural network prediction rate is independent of gender.

Introduction Engineering programs typically attract the top graduates from high school in terms of grade point average (GPA) and standardized test scores, but attrition out of engineering continues to be a major issue; programs often see some of the most statistically qualified students leave engineering for other majors or drop out of college altogether. In 1975, attrition in the freshman year in engineering was about 12%, increasing to about 25% by 1990 (Beaufait, 1991). In a large study of over 25,000 students at over 300 universities, Astin (1993) found that only 47% of students who begin in engineering graduate with an engineering degree. The National Academies’ report “Rising Above The Gathering Storm: Energizing and Employing America for a Brighter Economic Future” reports that undergraduate programs in science and engineering have the lowest retention rate among all academic disciplines. The National Academies describes the importance of advances in engineering and technology as crucial to the social and economic conditions for the United States to compete, prosper, and be secure in the global community in the 21st century (Augustine, 2005).

Imbrie, P., & Lin, J. J., & Oladunni, T., & Reid, K. (2007, June), Use Of A Neural Network Model And Noncognitive Measures To Predict Student Matriculation In Engineering Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. 10.18260/1-2--3029

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