2025 ASEE Annual Conference & Exposition

BOARD #106: Investigating Factors Influencing Performance in an Introductory Programming Course

Presented at Computing and Information Technology Division (CIT) Poster Session

Identifying factors that influence undergraduate students’ performance in an introductory programming (CS1) course can enable educators to optimize student success. It has become increasingly important for educators to understand and provide customized aid to their students in formal learning environments. In fast-paced and large-class environments, educators face challenges in identifying students’ learning needs based on diverse demographic and academic profiles. This study investigates how Gender, Prior Programming Experience (PPE), and Grade Point Average (GPA) impact student success in CS1 courses. The dataset consists of 836 students from six semesters (Spring 2021 to Fall 2023), with demographic and prior experience information collected through surveys. Although in the past, researchers have predicted student success using interaction with course material, previous exam scores, and a combination of many other factors, there is a gap in the literature for using specifically the combination of the pre-course factors of PPE, GPA, and gender to predict performance. Using correlation analysis, logistic regression, and Chi-Square tests, we explored the relationships between these factors and student performance, particularly in predicting whether a student would achieve above or below 80% in course exams before the student starts the course. The logistic regression model achieved a 76% accuracy, with higher precision and recall for identifying students scoring below 80%. GPA showed the strongest positive correlation with performance, while PPE and Gender also exhibited statistically significant relationships, though Gender's impact was minimal. These findings suggest that GPA and PPE are useful predictors for early identification of students at risk of under performance, helping educators develop targeted strategies to support students in programming courses.

Authors

Amanda Nicole Smith University of Florida [biography]

Amanda is an undergraduate student pursuing a Bachelor of Science in Computer Science at the University of Florida, with an expected graduation in Spring 2025. Her research interests focus on computer science education, particularly how educators can use machine learning models to provide real time intervention strategies to optimize individual student outcomes. This paper is a reflection of her commitment to improving educational strategies and fostering an inclusive learning atmosphere within the engineering community. As a Computer Science major, Amanda is focused on making positive impact through software engineering and machine learning.
Sage Bachus University of Florida [biography]

Sage Bachus is a fourth-year Mechanical Engineering and Pre-Med student at the Herbert Wertheim College of Engineering, University of Florida. His main research focus is in learning analytics and developing a way to better understand the underlying intricacies of how students learn and perform.
Ashish Aggarwal http://orcid.org/https://0000-0002-8365-3810 University of Florida [biography]

Ashish Aggarwal is an Instructional Associate Professor of Computer Science in the Department of Engineering Education at the University of Florida's Herbert Wertheim College of Engineering. His research operates at the intersection of Computer Science Education, Learning Analytics, and Artificial Intelligence, focusing on enhancing programming education through innovative technologies and pedagogical approaches. He is currently developing AI-powered tools for student assistance and assessment in large programming courses. He holds a Master's in Computer Science and Management from the University of Florida and a Bachelor's in Computer Science and Engineering from Jaypee University of Information Technology, India.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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