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

\bibitem{asee_peer_35074}
  Cardona, T. A., \& Cudney, E. A., \& Snyder, J., \& Hoerl, R. W. (2020, June), \emph{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

Tatiana A. Cardona, Elizabeth A. Cudney, Jennifer Snyder, and Roger Wesley Hoerl.  "Predicting Student Degree Completion Using Random Forest".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/35074 Internet.  20 Dec, 2024

\bibitem{asee_peer_35074}
  Tatiana A. Cardona, Elizabeth A. Cudney, Jennifer Snyder, and Roger Wesley Hoerl.
  "Predicting Student Degree Completion Using Random Forest".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/35074 Internet.  20 Dec, 2024

@INPROCEEDINGS{asee_peer_35074
author = "Tatiana A. Cardona, Elizabeth A. Cudney, Jennifer Snyder, and Roger Wesley Hoerl"
title = "Predicting Student Degree Completion Using Random Forest"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/35074}
number = {10.18260/1-2--35074}
}

TY  - CPAPER
AB  - 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.
AU  - Tatiana A. Cardona
AU  - Elizabeth A. Cudney
AU  - Jennifer Snyder
AU  - Roger Wesley Hoerl
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Predicting Student Degree Completion Using Random Forest
UR  - https://peer.asee.org/35074
DO  - 10.18260/1-2--35074
ER  -