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The Relationship Between Course Assignments and Academic Performance: An Analysis of Predictive Characteristics of Student Performance

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016





Conference Session

Engineering Economy Division Technical Session 3

Tagged Division

Engineering Economy

Tagged Topic

ASEE Diversity Committee

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Paper Authors


Deborah Ann Pedraza Texas Tech University Orcid 16x16

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I am a Systems and Engineering doctoral student at Texas Tech University. I have Bachelor's degree in the Mathematics from The University of Houston - Victoria, an MBA - The University of Houston - Victoria, and a Master's Degree in Electrical and Computer Engineering - The University of Massachusetts- Amherst. I teach Mathematics, Engineering, and Computer Science at Cuero High School in Cuero, TX and adjunct for The Victoria College in Victoria, TX. I also spent 20 years at Alcoa - Point Comfort Operations where I spent time as a Systems Analyst, Process Control Engineer, and Electrical Engineering and Computer Systems Superintendent. I am a former graduate of the Golden Crescent Alliance for Minorities in Engineering (GCAME) and then later returned to chair this organization for 15 years to help others consider engineering as a career.

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Mario G. Beruvides P.E. Texas Tech University

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Dr. Mario G. Beruvides is the AT&T Professor of Industrial Engineering and Director of the Laboratory for Systems Solutions in the Industrial Engineering Department at Texas Tech University. He is a registered professional engineer in the state of Texas. He holds a BS in mechanical engineering and an MSIE from the University of Miami in Coral Gables, Florida and a PhD in Industrial and Systems Engineering from Virginia Polytechnic Institute and State University (Virginia Tech) in Blacksburg, Virginia.

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Research in the open liturature suggests that several possible variables (i.e. SAT scores and others) can predict the academic readiness of students in an engineering program, but many are not always reliable sources. This research analyzes a model for predicting success in an Engineering Economics course as well as identifies factors that affect success or failure. This study identifies participants over a span of ten years attending Texas Tech University to establish base data. The study evaluates the data to determine at what point in the semester final grades can be accurately predicted. The study reviews the determinants that most influence the success or failure of students. The premise of this research is to accurately predict the performance of Engineering students and provide a means to identify struggling students and suggest intervention strategies for intervention to help students succeed. By identifying students that are more likely to fail and assisting them before they do, it is possible to increase the number of students that remain in Engineering.

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