Unlocking Student Potential With TA-Bot: Timely Submissions and Improved Code Style

Document Type

Conference Proceeding

Publication Date

2025

Publisher

Association for Computing Machinery (ACM)

Source Publication

SIGCSETS 2025: Proceedings of the 56th ACM Technical Symposium on Computer Science Education

Source ISSN

979-8-4007-0531-1

Original Item ID

DOI: 10.1145/3641554.3701955

Abstract

For students learning to write code, developing strong foundational coding skills and cultivating proper code style early on is crucial for success in subsequent courses and professional work. TA-Bot, an automated assessment tool, incorporates novice-friendly style suggestions wrapped around an industry-standard static analysis tool, code correctness testing, and an innovative rate-limiting system called Time Between Submissions ("TBS''). This system works in conjunction with a gamified incentive mechanism designed to motivate students to start weekly assignments earlier. Our hypothesis posited that this incentive, when combined with the inherent effects of TBS, would not only encourage students to initiate assignments sooner but would also prompt them to address more style-related issues and produce higher quality code. The TBS system resulted in a substantial and positive change in student submission patterns. Students began their work earlier, resulting in a higher number of code style issues resolved. When employing dynamic rate limiting, students not only rectified more style errors, but also produced superior quality submissions, leading to faster assignment completion compared to the control group. Additionally, we observed a positive impact on student code style as the semester progressed, despite the increasing complexity of assignments. Lastly, we highlight a significant proportion of students who exhibited continuous improvement in their code style, even after successfully passing all correctness test cases. Most notably, we successfully motivated students to improve code style even without a direct grade incentive.

Comments

Published as part of SIGCSETS 2025: Proceedings of the 56th ACM Technical Symposium on Computer Science Education (2025): 346-352. DOI.

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