2026 ASEE Annual Conference & Exposition

This Complete Research Paper focuses on factors that influence a student’s rate of success in a 100-level computer science course. Computer Science education has evolved significantly in recent years as an effort to expand access and to encourage students to pursue careers in related technologies. Although before the 21st century, it might have been uncommon to see a student at the K-12 level write a program simple enough to output “Hello World”, today students are creating algorithms that tell stories, interact with a user through a mobile app or control a robot to complete a series of tasks. With these new opportunities, especially in secondary education, it raises the question as to how this experience translates to success at the collegiate level, particularly in a 100-level computer science course. In addition, how do factors such as race, gender and highest achieved math level affect a student’s success? This case study acts as a follow up to a previous study that focused on Michigan high school graduates pursuing an engineering degree at a regional university and enrolled in a semester long 100-level applied programming course offered by the engineering department. The primary method employed for this study was survey research. As with the previous study, participants were asked to self-report their prior computer science experience such as course or activity as well as the types of programming languages they may have worked with. In addition, the new study allowed participants to provide demographic information such as geographic region of residence, race, gender, and math enrollment. The study was also expanded to include participants enrolled in an equivalent 100-level course offered through the university’s computer science department as well as a new “stretch” version of the course offered by the engineering department. The results were analyzed using logistic regression to determine if any of the factors of interest had an influence on a student’s success in the course (achievement of a “C” or higher). Overall, it was found that the three statistically significant factors were 1) the course the student was enrolled in, 2) whether the student had prior computer science experience, and 3) whether the student represented a historically minoritized population. Using a Wald’s test, it was determined that students enrolled in the standard pace course offered in the computer science department were between 0.051 and 0.586 times less likely to fail than those enrolled in the equivalently paced course offered by the engineering department. In terms of prior computer science experience, students that entered any of the courses without prior experience were between 1.04 and 10.10 times more likely to fail than students that had any type of experience. Finally, students representing historically minoritized populations were between 1.27 and 6.29 times more likely to fail a 100-level computer science course compared to their white counterparts. These two later results are especially concerning since 45% of all participants (49% from historically minoritized populations) reported not having prior experience despite nearly 70% of public high schools in the state of Michigan offering a foundational computer science course. If gaining even some type of computer science experience during a student’s K-12 career is a leading factor in whether a student is successful in the subject at a collegiate level, then more work needs to be done to prepare students. This may be by providing more access to experience with computer science as well as coaching students to increase their digital literacy as they consider pursuing degrees in engineering or computer science.