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Correlation of Admission Data to Undergraduate Student Success in Electrical Engineering

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Electrical and Computer Division Technical Session 2

Tagged Division

Electrical and Computer

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Paper Authors


Harry O. Aintablian University of Washington, Bothell

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Harry Aintablian is a Lecturer of Electrical Engineering. He has a Ph.D. in Electrical and Computer Engineering from Ohio University. He has eighteen years of experience in aerospace power electronics/power systems at Jet Propulsion Laboratory and at Boeing Space Systems. He has five years of full-time teaching experience in electrical engineering. His research interests include the application of power electronics to space systems and to alternative energy systems.

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Tadesse Ghirmai University of Washington, Bothell Orcid 16x16

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Dr. Tadesse Ghirmai obtained his Ph.D. degree in electrical engineering in 2004 from Stony Brook University, New York, USA. Currently, he works as an assistant professor in the electrical engineering program of the University of Washington Bothell. In addition to his research interest in engineering education, Dr. Ghirmai works in the areas of communications and statistical signal processing with emphasis on system modeling, estimation of parameters and detection of signals. He has extensively worked on Bayesian signal processing methods, particularly, on sequential Monte Carlo techniques. Dr. Ghirmai has received the best paper award in 2007 for a paper he coauthored on IEEE Signal Processing Magazine.

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A weighted average score is used as admissions criterion for undergraduate electrical engineering students. The score comprises calculus, physics and chemistry grades, overall student GPA and a rating of the student’s personal statement. This paper presents statistical data to show how well student success in electrical engineering is correlated with admissions criteria. Results of regression analysis show that there is positive correlation between sophomore-level electrical engineering course grades and weighted average admission scores. The results also show that calculus grades are strong predictors, while overall student GPAs are weak predictors, of electrical engineering course GPA. The paper sheds light on the results of the study and makes recommendations for improvement in admissions.

Aintablian, H. O., & Ghirmai, T. (2017, June), Correlation of Admission Data to Undergraduate Student Success in Electrical Engineering Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28077

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