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Predicting Academic Success For First Semester Engineering Students Using Personality Trait Indicators

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2008 Annual Conference & Exposition


Pittsburgh, Pennsylvania

Publication Date

June 22, 2008

Start Date

June 22, 2008

End Date

June 25, 2008



Conference Session

Focus on IE Course Design and Assessment

Tagged Division

Industrial Engineering

Page Count


Page Numbers

13.990.1 - 13.990.11



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Paper Authors


Paul Kauffmann East Carolina University

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Paul J. Kauffmann is Professor and Chair in the Department of Engineering at East Carolina University. His industry career included positions as Plant Manager and Engineering Director. Dr. Kauffmann received a BS degree in Electrical Engineering and MENG in Mechanical Engineering from Virginia Tech. He received his Ph.D. in Industrial Engineering from Penn State and is a registered Professional Engineer.

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Cathy Hall East Carolina University

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Cathy W. Hall is a professor in the Department of Psychology at East Carolina University. She received her BA degree in psychology from Emory University and a Ph.D. in from University of Georgia. Her experience includes positions as a school psychologist, director of the school psychology program at Fort Hays State University, and psychologist in Kelly Psychological Service Center. Her research interest include resiliency in relation to adjustment and metacognition.

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Gene Dixon East Carolina University

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Gene Dixon is an assistant professor in the Department of Engineering at East Carolina University. He received a BS in Material Engineering from Auburn University, an MBA from Nova Southeastern and a PhD in Industrial and System Engineering and Engineering Management from the University of Alabama – Huntsville. His professional experience includes positions with Chicago Bridge and Westinghouse. General research interests focus on engineering management and related processes. Specific interests include the role of leaders and followers in the leadership process.

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John Garner East Carolina University

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John Dail Garner is a Lecturer in the Department of Engineering at East Carolina University. Mr. Garner received an MS in Mechanical Engineering from North Carolina State University. His research interests include recruitment and retention in engineering education, as well as energy production and conservation in agriculture.

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

Predicting Academic Success for First Semester Engineering Students Using Personality Trait Indicators


The dual factors of attracting and retaining talented students in the areas of science, technology, engineering and mathematics (STEM) are critical issues for building the technology work force. When students enter colleges/universities and declare an engineering major, retention becomes the primary focus. Retention of talented students is a significant issue in engineering programs and improvement of retention rates can be a powerful tool in increasing the number of engineering graduates needed for national and global competitiveness. A number of studies have examined predictors of success for entering freshman engineering students including SAT scores and high school performance. The goal of this present work is to identify other personality factors that are critical for retention. Knowing this information, timely and targeted intervention can be applied to improve student success. The area of internal motivation is often proposed as a success factor and generally studies have neglected this area due to the difficulty in measuring and evaluating. This study considers the results of the Big Five and locus of control tests given to a group of first semester engineering freshmen. Factors of these tests were evaluated as tools to measure student motivation to succeed. The levels of these traits were then employed in a multifactor linear regression model to predict overall grade point average for the first semester. The study found that two of the Big Five factors along with locus of control were significant prediction variables for first semester grade point average.


Retention of engineering students is a continuing concern among university academic programs nationwide. In improving retention, engineering educators have spent significant effort in identifying relationships between various measures of success and prediction variables in the hope of identifying focused interventions to improve student success.

A variety of multi-variable models have been developed to predict various measures of student success using a range factors. These studies examined the use of high school grade point averages (GPAs) and scores on standardized tests to predict student performance.1, 2, 3 In assessing the field of engineering in particular, Takahira, et al.4, found that the primary factors associated with persistence in an engineering statics course were GPA and SAT-math scores. Another study reported a positive effect of an entrepreneurship program on GPA and retention.5 Other models have been more complex. Student success and persistence were examined by French et al. using hierarchical linear regression.6 They examined both quantitative variables (SAT scores, high school rank, university cumulative grade point average) and qualitative variables (such as academic motivation and institutional integration). For measures of success they used junior and senior GPA, university enrollment and major enrollment over six and eight semesters. The study found that SAT scores, high school rank, and gender were significant predictors of GPA and that an orientation course was not a significant factor in predicting college success.

Kauffmann, P., & Hall, C., & Dixon, G., & Garner, J. (2008, June), Predicting Academic Success For First Semester Engineering Students Using Personality Trait Indicators Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--4404

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2008 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015