Exploring the Link Between Prerequisites and Performance in Advanced Data Structures

Published in SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 2020

Recommended citation: Sophia Krause-Levy, Sander Valstar, Leo Porter, and William G. Griswold. 2020. Exploring the Link Between Prerequisites and Performance in Advanced Data Structures. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE). 386–392. doi/10.1145/3328778.3366867

Recent work has identified a mismatch between instructor expectations of students’ mastery of prerequisite course content and their actual ability. This invites the question of why this mismatch exists. We first examined grades in prerequisite courses and found they meaningfully correlated with performance on an assessment testing their knowledge of prerequisite material. In addition, we found neither taking alternatives to the primary identified prerequisites nor the delay between taking prerequisite courses and the follow-on course meaningfully impacts performance. Second, we confirmed that prerequisite course grades are significantly correlated with the grade in the follow-on course-confirming that the grades in the previous courses convey some information about student understanding of those topics. Perhaps surprisingly, we found that grades in courses outside computing were similarly correlated as those courses inside computing, suggesting that underlying factors such as general study skills may be as important as the domain-specific knowledge itself.