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Regression Analysis to Predict Student Electric Circuits Performance

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Assessment of Learning in ECE Courses

Tagged Division

Electrical and Computer

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Paper Authors


Matthew Garett Young Arkansas Tech University

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Matthew G. Young received his B. Sc. in Electrical Engineering from Arkansas Tech University in 2009. He participated in an NSF IREU focusing on antenna design in the summer of 2009 before obtaining his M. Sc. in Microelectronics-Photonics at the University of Arkansas in 2012. For his M. Sc. studies, he focused on the growth of silicon nanowires via plasma enhanced chemical vapor deposition. In August of 2016, he joined the faculty at Arkansas Tech University as an Assistant Professor of Electrical Engineering. His Ph.D. was completed at the University of Arkansas in May 2017. At Arkansas Tech University, Matthew is focused on establishing research experiences in photovoltaics for undergraduate and graduate students and investigating new methods to enhance engineering education in the classroom.

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Edward Carl Greco Jr. Arkansas Tech University

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Dr. Greco is a Professor of Electrical and Computer Engineering with research interest in biomedical signal processing. He teaches courses in digital systems, signals and systems, communications and biomedical signal processing.

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Scott Marks Jordan Arkansas Tech University

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Thomas Galen Limperis Arkansas Tech University

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We have developed a proportional odds logistic regression (POLR) model in the R programming language that can provide supporting data to aid in engineering student retention. The primary goal of the regression analysis is to identify populations of students who are at most risk of not succeeding (making lower than a grade of ‘C’) in their undergraduate electrical engineering courses. Electric Circuits is a cornerstone course in many electrical engineering curriculums. Pre-requisite course performance can be an indicator of future course performance for undergraduate students. Following this logic, it would not be surprising to think that Calculus I and Calculus II might be strong predictors of Electric Circuits I performance. However, the POLR we have employed shows that Calculus I and II are relatively weak predictors of Electric Circuits I performance whereas cumulative GPA seems to be the strongest predictor. This might not be a surprise to those that have suggested that the overall content of Electric Circuits I consists mainly of algebra. Nonetheless, one would think that a student who is proficient at algebra would also be skilled at Calculus I and II, and thus Electric Circuits I via the pre-requisite argument. While our model does not discount that strong student performance in Calculus I and II also correlate to strong performance in Electric Circuits I, it does provide convincing evidence that cumulative GPA is the best metric to examine for predicting student success. The present research is an expansion of research presented at the ASEE 2018 Midwest Regional conference. Also, we have incorporated additional analysis techniques to determine how factors are correlated, thus potentially increasing the prediction capability of success in the model.

Young, M. G., & Greco, E. C., & Jordan, S. M., & Limperis, T. G. (2019, June), Regression Analysis to Predict Student Electric Circuits Performance Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33231

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