Keep It Relevant! Using In-class Exercises to PredictWeekly Performance in CS1
Abstract
In large programming courses, it can be difficult for instructors to identify students who need help. Often the earliest indication of trouble is when a student fails an exam, which unfortunately can be too late. Using data from 7 sections of CS1 over multiple semesters, we show that performance on lab and in-class coding exercises can be used to accurately predict which students will fail or struggle on upcoming weekly lab assignments. We found that recent relevant in-class coding exercises were the best features for building accurate models. This approach has potential in helping CS1 instructors identify students who need help, determine which topics need additional attention, and formulate intervention plans, all on a weekly basis before each lab meeting.
Publication Title
SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
Recommended Citation
Hicks, E., Cook, A., Malasri, K., Zaman, A., & Phan, V. (2022). Keep It Relevant! Using In-class Exercises to PredictWeekly Performance in CS1. SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, 1, 154-160. https://doi.org/10.1145/3478431.3499357
