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

\bibitem{asee_peer_33183}
  Cardona, T. A., \& Cudney, E. A., \& Snyder, J. (2019, June), \emph{Predicting Degree Completion through Data Mining}
  Paper presented at 2019 ASEE Annual Conference \& Exposition , Tampa, Florida.
  10.18260/1-2--33183

Tatiana A. Cardona, Elizabeth A. Cudney, and Jennifer Snyder.  "Predicting Degree Completion through Data Mining".  2019 ASEE Annual Conference & Exposition , Tampa, Florida, 2019, June.  ASEE Conferences, 2019.  https://peer.asee.org/33183 Internet.  20 Dec, 2024

\bibitem{asee_peer_33183}
  Tatiana A. Cardona, Elizabeth A. Cudney, and Jennifer Snyder.
  "Predicting Degree Completion through Data Mining".
  \emph{2019 ASEE Annual Conference \& Exposition , Tampa, Florida, 2019, June}.
  ASEE Conferences, 2019.
  https://peer.asee.org/33183 Internet.  20 Dec, 2024

@INPROCEEDINGS{asee_peer_33183
author = "Tatiana A. Cardona, Elizabeth A. Cudney, and Jennifer Snyder"
title = "Predicting Degree Completion through Data Mining"
booktitle = "2019 ASEE Annual Conference \& Exposition "
year = "2019"
month = "June"
address = "Tampa, Florida"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/33183}
number = {10.18260/1-2--33183}
}

TY  - CPAPER
AB  - 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.
AU  - Tatiana A. Cardona
AU  - Elizabeth A. Cudney
AU  - Jennifer Snyder
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Predicting Degree Completion through Data Mining
UR  - https://peer.asee.org/33183
DO  - 10.18260/1-2--33183
ER  -