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Predicting Degree Completion through Data Mining

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Engineering Management Division Technical Session 2

Tagged Division

Engineering Management

Page Count

9

DOI

10.18260/1-2--33183

Permanent URL

https://peer.asee.org/33183

Download Count

249

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Paper Authors

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Tatiana A. Cardona Missouri University of Science and Technology

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Tatiana A. Cardona is a Ph.D. candidate in Systems engineering at Missouri University of Science and Technology (MS&T)from where she also received her M.S. in Engineering Management in 2006. Tatiana completed her B.S. in Industrial Engineering at Technological University of Pereira, Colombia in 2009. from the same institution. Her research interests include statistical modeling, Operations research and Data Science. She has served as a head teaching assistant for four semesters in operations management and project management in the MS&T.

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Elizabeth A. Cudney Missouri University of Science & Technology

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Dr. Elizabeth Cudney is an Associate Professor in the Engineering Management and Systems Engineering Department at Missouri University of Science and Technology. She received her B.S. in Industrial Engineering from North Carolina State University, Master of Engineering in Mechanical Engineering and MBA from the University of Hartford, and doctorate in Engineering Management from the University of Missouri – Rolla. In 2018, Dr. Cudney received the ASQ Crosby Medal for her book on Design for Six Sigma. Dr. Cudney received the 2018 IISE Fellow Award. She also received the 2017 Yoshio Kondo Academic Research Prize from the International Academy for Quality for sustained performance in exceptional published works. In 2014, Dr. Cudney was elected as an ASEM Fellow. In 2013, Dr. Cudney was elected as an ASQ Fellow. In 2010, Dr. Cudney was inducted into the International Academy for Quality. She received the 2008 ASQ A.V. Feigenbaum Medal and the 2006 SME Outstanding Young Manufacturing Engineering Award. She has published seven books and over 80 journal papers. Dr. Cudney is a certified Lean Six Sigma Master Black Belt. She holds eight ASQ certifications, which include ASQ Certified Quality Engineer, Manager of Quality/Operational Excellence, and Certified Six Sigma Black Belt, amongst others.

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Jennifer Snyder Valencia College

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Jennifer Snyder serves as the Dean of Science for Valencia College's East Campus. She earned a Ph.D. from the Department of Engineering Management and Systems Engineering at Missouri University of Science and Technology. She received her B.S. and M.S. in Chemistry from Missouri State University in Springfield, Missouri.

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Abstract

Universities and colleges continuously strive to increase student retention and degree completion. The U.S. Department of Education has set the goal of preparing a society with individuals capable to “understand, explore and engage with the world” specific skills that can be achieved through STEM majors. Currently, considerable student data are collected and there is a latent opportunity to make the available information useful for determining the factors that influence retention and completion rates. Analyzing student data with those aims is vital for intentional student advising. To this end, this research presents the application of decision trees to predict degree completion within three years for STEM community college students. Decision trees also enable the identification of the factors that impact program completion using non-parametric models by classifying data using decision rules from the patterns learned. The model was developed using data on 283 students with 14 variables. The variables included age, gender, degree, and college GPA, among others. The results offer important insight into how to develop a more efficient and responsive system to support students.

Cardona, T. A., & Cudney, E. A., & Snyder, J. (2019, June), Predicting Degree Completion through Data Mining Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33183

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