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Predicting Student Degree Completion Using Random Forest

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

EMD 2: Issues in Engineering Management Education

Tagged Division

Engineering Management

Page Count

12

DOI

10.18260/1-2--35074

Permanent URL

https://peer.asee.org/35074

Download Count

166

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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 2016. Tatiana completed her B.S. in Industrial Engineering at Technological University of Pereira, Colombia in 2009. 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 and 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 eight books and over 85 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 received a PhD in Engineering Management at Missouri University of Science and Technology. She received her B.S. and M.S. in Chemistry from Missouri State University in Springfield, Missouri. She is a dean of science for Valencia College in Orlando, Florida.

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Roger Wesley Hoerl Union College

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Dr. Roger W. Hoerl is the Brate-Peschel Associate Professor of Statistics at Union College in Schenectady, NY. Previously, he led the Applied Statistics Lab at GE Global Research. While at GE, Dr. Hoerl led a team of statisticians, applied mathematicians, and computational financial analysts who worked on some of GE’s most challenging research problems, such as developing personalized medicine protocols, enhancing the reliability of aircraft engines, and management of risk for a half-trillion dollar portfolio. Dr. Hoerl has been named a Fellow of the American Statistical Association and the American Society for Quality, and has been elected to the International Statistical Institute and the International Academy for Quality. He has received the Brumbaugh and Hunter Awards, as well as the Shewhart Medal, from the American Society for Quality, and the Founders Award and Deming Lectureship Award from the American Statistical Association. While at GE Global Research, he received the Coolidge Fellowship, honoring one scientist a year from among the four global GE Research and Development sites for lifetime technical achievement.

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Abstract

Recent reports indicate that 40 percent of freshman will not graduate. Therefore, increasing student retention rates in higher education is great importance. Student retention is a measure of an institutions’ ability to prepare students with specific skills that contribute to society. To improve retention rates colleges and universities require internal strategies for intentional advising to ensure that students are able to complete their majors timely. Currently, efforts have been made to adjust admission requirements; however, the retention rates are still considered low (62% for colleges and 60% for universities) and such strategies have reduced access from different economic sectors to higher education. Thus, institutions have recognized the need of understanding the factors that impact retention to better focus their efforts. To this end, this research presents the application of random forest to predict degree completion within three years, 150 percent time completion for an associate degree, and identify the variables that impact student retention at a large community college on the East Coast. Random forest enables the classification of the input variables into expected classes, completion and not completion. The algorithm consists of bagging decision trees created randomly from the training sample, thus creating a forest. Each tree gives insight into the variables that have more impact, which generates a vote that informs which are the most important variables in the class prediction. Thus, a final decision tree is created with such records obtaining the best performance that can be achieved. The model was developed using data on 282 students with 14 variables. The variables included age, gender, degree, and college GPA, among others. The model results, which include prediction and variables ranking, offer an important understanding about how to develop a more efficient and responsive system to support students.

Cardona, T. A., & Cudney, E. A., & Snyder, J., & Hoerl, R. W. (2020, June), Predicting Student Degree Completion Using Random Forest Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35074

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