Asee peer logo

Understanding Factors of Engineering Student Persistence Using Predictive Modeling

Download Paper |

Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Engineering Design Graphics Division Technical Session 4: A Potpourri of Ideas

Tagged Division

Engineering Design Graphics

Tagged Topic

Diversity

Page Count

7

Permanent URL

https://peer.asee.org/37957

Download Count

12

Request a correction

Paper Authors

biography

Daniel P. Kelly Texas Tech University Orcid 16x16 orcid.org/0000-0002-6463-7022

visit author page

Dr. Daniel P. Kelly is an Assistant Professor of STEM education at Texas Tech University in the Department of Curriculum and Instruction. He earned his doctorate in Technology Education from North Carolina State University where he also served on the faculty. Previously, he worked as a middle and high school science, technology, and engineering teacher in North Carolina. Dr. Kelly serves as the Associate Editor of the Engineering Design Graphics Journal and Editor-in-Chief and Founder of the Journal of Foster Care. Dr. Kelly studies how STEM education and engagement can improve the educational outcomes of students at risk of not completing high school due to academic, behavioral, or social needs. Of particular interest are children in foster care and other non-parental custody arrangements.

visit author page

biography

Jeremy V. Ernst Embry-Riddle Aeronautical University

visit author page

Dr. Jeremy Ernst is Professor of Technology and Associate Chancellor for Research within the Worldwide Campus at Embry-Riddle Aeronautical University. He has had prior academic and administrative appointments at Virginia Tech as well as North Carolina State University. His efforts center on curriculum research and development in STEM education to provide evidence-based models that promote engagement, development of cognitive competency sets, and performance-based application abilities of students at-risk.

visit author page

biography

Aaron C. Clark North Carolina State University at Raleigh

visit author page

Aaron C. Clark is a Professor of Technology, Design, and Engineering Education within the College of Education, as well as the Department Head for the Department of Science, Technology, Engineering and Mathematics Education. He has worked in both industry and education. Dr. Clark's teaching specialties are in visual theory, 3-D modeling, technical animation, and STEM-based pedagogy. Research areas include graphics education, game art and design, scientific/technical visualization and professional development for technology and engineering education. He is a Principle Investigator on a variety of grants related to visualization and education and has focused his research in areas related to STEM curricula integration.

visit author page

biography

Erik Schettig North Carolina State University at Raleigh

visit author page

Erik is a Ph.D. student in the Learning and Teaching in STEM program at NC State University with a focus on Engineering and Technology. He has over 10 years of K-12 teaching and education outreach experience. In addition, Erik has been a director for STEM Summer programs and lead instructor for NC State pre-college programs.

visit author page

Download Paper |

Abstract

Student persistence in higher education is a topic of discussion in the academic literature and within our colleges and universities. This is especially relevant as university programs continue to focus on equity, inclusion, and support for student populations that are historically underrepresented in higher education and within specific disciplines. Engineering education has been attempting to address these issues for some time and with the graduation rates for engineering programs averaging up to 50%, understanding why students stay or leave these programs is crucial information. The reasons students persist or leave higher education programs are important data points for any university program. However, traditional statistical analysis methods may not be robust or accessible enough to understand and communicate these factors. To determine these factors, machine learning and predictive analysis software was employed to examine these factors of persistence for engineering education students. Dozens of variables including academic scores, non-cognitive and skill-based assessments, and demographic information for 300 students in an introductory engineering graphics course were used to develop a model capable of predicting whether a student will persist with nearly 94% accuracy. This research indicated that age, gender, and self-efficacy, and parental career were the most influential factors of persistence. This paper will discuss the underlying reasons for these factors being important contributing variables to student persistence in engineering as well as the specific effects they may have on sub-populations and nested groups. Using this information, combined with the theoretical underpinnings of these constructs, may provide areas on which to focus and specifically target to improve persistence rates in engineering education.

Kelly, D. P., & Ernst, J. V., & Clark, A. C., & Schettig, E. (2021, July), Understanding Factors of Engineering Student Persistence Using Predictive Modeling Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37957

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2021 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015