2023 ASEE Annual Conference & Exposition

Predicting Student Success in College Algebra Classes Using Machine Learning

Presented at STEM Education at the Two-Year College

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.

Authors

Dr. Zeynep Akcay Ozkan http://orcid.org/0000-0003-2530-2761 City University of New York, Queensborough Community College [biography]

Dr. Zeynep Akcay Ozkan is an Associate Professor of Mathematics at Queensborough Community College of the City University of New York. She received her PhD in Applied Mathematics from the joint program at New Jersey Institute of Technology and Rutgers Universities (2014), with concentration on Mathematical and Computational Neuroscience. She also holds an MS degree in Financial Mathematics from Florida State University (2009). Dr. Akcay Ozkan’s research interests include mathematical neuroscience, math education and data science.
Yuanhong Yu City University of New York, Queensborough Community College
Dr. Ewa Stelmach City University of New York, Queensborough Community College [biography]

Ewa Stelmach received her Ph.D. in Mathematics Education from Columbia University in 2019, a M.Phil. from Columbia University in 2017, a M.S in Applied Mathematics from Hofstra University in 2011 and B.S. in Mathematics from Stony Brook University in 2009.
She has been teaching mathematics at Queensborough Community College, CUNY since 2011. She is always looking for innovative ways to teach her classes to inspire her students and enhance their learning experience. Over the years she participated in many departmental committees to help improve students’ experience. Ewa Stelmach is a co-author of the Open Resource Educational textbook for College Algebra students. She is also the administrator and author of many problems in WeBWork, a free homework platform.
Her interests include college-level teaching, mathematics education, and teaching with technology.

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