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Formulating Predictive Models of Engineering Student Throughput

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Conference

2013 ASEE Annual Conference & Exposition

Location

Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013

ISSN

2153-5965

Conference Session

Issues in Advising and Mentoring

Tagged Division

Educational Research and Methods

Page Count

10

Page Numbers

23.611.1 - 23.611.10

Permanent URL

https://peer.asee.org/19625

Download Count

59

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Paper Authors

biography

Gillian M. Nicholls University of Alabama in Huntsville

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Dr. Gillian M. Nicholls is an Assistant Professor of Industrial and Systems Engineering and Engineering Management, and a 2009-2010 Gray Faculty Fellow at the University of Alabama in Huntsville. Her research interests are in applying statistical analysis and optimization to supply chain management, transportation management, and engineering education. She holds the B.S. in Industrial Engineering (Lehigh University), Masters in Business Administration (Penn State University), M.S. in Industrial Engineering (University of Pittsburgh.), and Ph.D. in Industrial Engineering (University of Pittsburgh). Address: N149 Technology Hall, The University of Alabama in Huntsville, Huntsville, AL 35899; telephone (256) 824-6637; fax: (256) 824-6733; e-mail: gillian.nicholls@uah.edu.

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Abstract

Formulating Predictive Models of Engineering Student ThroughputEngineering degree acquisition is a complex system that lacks tools for efficient management and goaloptimization. A reliable model of engineering degree acquisition will help administrators to increasethroughput and resource utilization. It will also aid engineering students in better managing theireducational investment. A method is needed to quantitatively assess the factors that predict time tograduation for engineering students; explore the potential positive effects of intervention to affect criticalfactors; and examine the costs vs. benefits of increasing engineering student throughput rates.Universities are under increasing pressure to educate engineering students more effectively so thatstudents are likely to graduate within four years. Changes in student course-taking patterns and degreerequirements have led to a lengthening of the time to graduation for typical engineering students. Thisreduces the number of students that can be effectively educated in a four year period of time andconsumes additional resources in course enrollments, faculty time, and support staff labor. Given tuitioncosts that have risen at a rate exceeding the rate of inflation, the trends have undesirable results for bothuniversities and students.This paper discusses the development of a research design to model student progression throughengineering degree acquisition as a complex system. Elements will include transition probabilities,identifying critical factors, predicting time to graduation, estimating costs and benefits of potentialinterventions targeting the critical factors, and projecting the resulting throughput of engineers earningbachelor’s degrees. The main goal of the research is to achieve actionable, applicable, and accuratedecision modeling of a student’s progress through an engineering degree program and a university’sresulting throughput rate to provide strategic and financial decision-making tools for both students andadministrators. The longer term goal of the research is to increase STEM student persistence rates andimprove STEM throughput.

Nicholls, G. M. (2013, June), Formulating Predictive Models of Engineering Student Throughput Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/19625

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