Dorris, D. M., & Swann, J. L., & Ivy, J. (2021, July), A Data-driven Approach for Understanding and Predicting Engineering Student Dropout  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36575

\bibitem{asee_peer_36575}
  Dorris, D. M., \& Swann, J. L., \& Ivy, J. (2021, July), \emph{A Data-driven Approach for Understanding and Predicting Engineering Student Dropout}
  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference.
  10.18260/1-2--36575

Danika M. Dorris, Julie L. Swann, and Julie Ivy.  "A Data-driven Approach for Understanding and Predicting Engineering Student Dropout".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/36575 Internet.  13 May, 2024

\bibitem{asee_peer_36575}
  Danika M. Dorris, Julie L. Swann, and Julie Ivy.
  "A Data-driven Approach for Understanding and Predicting Engineering Student Dropout".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/36575 Internet.  13 May, 2024

@INPROCEEDINGS{asee_peer_36575
author = "Danika M. Dorris, Julie L. Swann, and Julie Ivy"
title = "A Data-driven Approach for Understanding and Predicting Engineering Student Dropout"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/36575}
number = {10.18260/1-2--36575}
}

TY  - CPAPER
AB  - This is a research paper focused on identifying influential factors of student dropout. Students who drop out of college can suffer negative effects on their wellbeing long after they leave college. Many of these dropout students are dropping out in the first two years which highlights the importance of identifying at-risk students early on. We analyzed data for the 1,754 students in the 2014 undergraduate engineering cohort at a large public university. Of these 1,754 students, 12.6 percent dropped out. Ninety-two factors describe each student’s application information (e.g., SAT), academic metrics, (e.g., course load, GPA), and demographic information (ethnicity, age). Defining characteristics and significant predictors of at-risk students were identified using three types of analyses: (i) statistical testing for comparisons, (ii) cluster analysis, and (iii) logistic regression predictions. We first identify significant differences between graduate and dropout populations with hypothesis testing. Then, we use clustering to identify subgroups within the cohort and label each group according to a set of defining characteristics. Lastly, significant predictors are extracted from a logistic regression model predicting eventual dropout. Statistical testing for comparisons found that there was a lower proportion of female and full-time students in the dropout population than those who graduated. Most dropout students formed a separate cluster from the rest of the cohort, and the time of dropout influenced the clusters formed within the dropout population. From the regression models, we learn that GPA and passed credits are significant predictors in the first year, and race does not become a significant predictor of dropout until the second year. The factors that influence dropout change over time which emphasize the importance of dynamic dropout prediction models. The findings from each phase of our analysis highlight the complexity of understanding the causes of dropout and the importance of personalizing interventions for specific populations within a cohort.
AU  - Danika M. Dorris
AU  - Julie L. Swann
AU  - Julie Ivy
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - A Data-driven Approach for Understanding and Predicting Engineering Student Dropout
UR  - https://peer.asee.org/36575
DO  - 10.18260/1-2--36575
ER  -