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. 10.18260/1-2--37957

\bibitem{asee_peer_37957}
  Kelly, D. P., \& Ernst, J. V., \& Clark, A. C., \& Schettig, E. (2021, July), \emph{Understanding Factors of Engineering Student Persistence Using Predictive Modeling}
  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference.
  10.18260/1-2--37957

Daniel P. Kelly, Jeremy V. Ernst, Aaron C. Clark, and Erik Schettig.  "Understanding Factors of Engineering Student Persistence Using Predictive Modeling".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/37957 Internet.  12 Jan, 2025

\bibitem{asee_peer_37957}
  Daniel P. Kelly, Jeremy V. Ernst, Aaron C. Clark, and Erik Schettig.
  "Understanding Factors of Engineering Student Persistence Using Predictive Modeling".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/37957 Internet.  12 Jan, 2025

@INPROCEEDINGS{asee_peer_37957
author = "Daniel P. Kelly, Jeremy V. Ernst, Aaron C. Clark, and Erik Schettig"
title = "Understanding Factors of Engineering Student Persistence Using Predictive Modeling"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/37957}
number = {10.18260/1-2--37957}
}

TY  - CPAPER
AB  - 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. 
 
AU  - Daniel P. Kelly
AU  - Jeremy V. Ernst
AU  - Aaron C. Clark
AU  - Erik Schettig
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Understanding Factors of Engineering Student Persistence Using Predictive Modeling
UR  - https://peer.asee.org/37957
DO  - 10.18260/1-2--37957
ER  -