Akcay Ozkan, Z., & Yu, Y., & Stelmach, E. (2023, June), Predicting Student Success in College Algebra Classes Using Machine Learning  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43933

\bibitem{asee_peer_43933}
  Akcay Ozkan, Z., \& Yu, Y., \& Stelmach, E. (2023, June), \emph{Predicting Student Success in College Algebra Classes Using Machine Learning}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43933

Zeynep Akcay Ozkan, Yuanhong Yu, and Ewa Stelmach.  "Predicting Student Success in College Algebra Classes Using Machine Learning".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/43933 Internet.  15 Dec, 2024

\bibitem{asee_peer_43933}
  Zeynep Akcay Ozkan, Yuanhong Yu, and Ewa Stelmach.
  "Predicting Student Success in College Algebra Classes Using Machine Learning".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/43933 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_43933
author = "Zeynep Akcay Ozkan, Yuanhong Yu, and Ewa Stelmach"
title = "Predicting Student Success in College Algebra Classes Using Machine Learning"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/43933}
number = {10.18260/1-2--43933}
}

TY  - CPAPER
AB  - College Algebra is a gateway course for STEM majors with large enrollment and low passing 
rates. We analyze the factors which contribute to student success in College Algebra courses at 
an urban community college. Characteristics and grades of over twenty thousand students who 
were enrolled in College Algebra courses between the years 2017 and 2022 have been analyzed. 
Among the students’ characteristics being studied are gender, ethnicity, age, first-generation 
college status, placement exam scores, grade point averages (GPA), whether they are freshmen 
or transfer students. The course modalities include online, hybrid or in-person. We study 
correlations between factors that affect student success. Using k-nearest neighbor and decision 
tree algorithms, we predict student success based on the student characteristics and course
features. Using Chi-Square Test of Independence, we show that passing rates of students depend 
on gender, ethnicity, age, overall GPA and whether they are freshmen or transfer students. 
Passing rates also depend on the modality of the course and the semester (fall or spring) the 
course is taken. With both supervised machine learning algorithms used, the probability of 
students passing were predicted with approximately 85 percent accuracy. Our results show that 
machine learning models can successfully be used on student data to predict course outcomes
which can enable early intervention to those students with higher chances of failure in the course. 
Our findings may encourage college administrations to use machine learning for predicting 
student success and be able to provide better advisement to incoming students regarding course 
selection.
AU  - Zeynep Akcay Ozkan
AU  - Yuanhong Yu
AU  - Ewa Stelmach
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Predicting Student Success in College Algebra Classes Using Machine Learning
UR  - https://peer.asee.org/43933
DO  - 10.18260/1-2--43933
ER  -