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Predictors Of Success In The First Two Years: A Tool For Retention

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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

IE Program Design I

Tagged Division

Industrial Engineering

Page Count


Page Numbers

12.1171.1 - 12.1171.10



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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 Univerisity. 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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Tarek Abdel-Salam East Carolina University

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TAREK ABDEL-SALAM is an Assistant Professor in the Department of Engineering at East Carolina University. Dr. Abdel-Salam received a Ph.D. in Mechanical from Old Dominion University. His research interests include educational effectiveness in engineering education, energy management, and thermal / fluid systems.

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John Dail 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



Retention is a significant issue in engineering education. The ability to identify factors in student records which best predict academic success can be a very important tool in developing and implementing the timely and focused interventions which are an essential part of a strategic plan to improve retention rates. This paper presents a study conducted to improve retention rates by using step wise regression to identify the most significant factors to predict undergraduate grade point average at the end of the freshman and sophomore years. The model examines standardized test scores, rank in high school class, and various measures of high school grade point average for three different years of performance. The results show that, for this sample of first and second year students, un weighted high school grade point average and rank in high school graduating class are the most important predictors of college grade point average success. Standardized test scores were not significant predictors.


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 this way, it may be possible to identify targeted interventions to improve success or prevent failure. As a result of these efforts, a variety of multi-variable models have been developed to predict various measures of student success using a range factors.

In one example, Takahira et al.1 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.2 Other researchers found scores from a non-technical, writing assignment was a predictor of academic success of freshmen engineering students as measured by cumulative grade point average after completion of the first two semesters.3

Other models have been more complex. Student success and persistence were examined by French et al.4 using hierarchical linear regression. 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 ineffective.

Zhang et al.5 evaluated a number of factors and their impact on engineering student success as measured by graduation rate. Using a multiple logistic regression model and data from nine institutions, they examined the impact on college graduation of high school GPA, gender, ethnicity, quantitative and verbal SAT scores, and citizenship and their impact on graduation.

Kauffmann, P., & Abdel-Salam, T., & Dail Garner, J. (2007, June), Predictors Of Success In The First Two Years: A Tool For Retention Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. 10.18260/1-2--2367

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