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Dynamic Rate Limiting with TA-Bot in CS1

Published: 06 March 2023 Publication History

Abstract

Automated Assessment Tools (AATs) have been used in undergraduate CS education for decades at many universities. TA-Bot, a modular AAT, has existed in some form for 25 years serving thousands of students across multiple universities. Previous research has shown that the earlier students start assignments, the better scores they receive. TA-Bot implements a novel dynamic rate limiting system to incentivize earlier student submissions. As the assignment deadline approaches, the cooldown from when a student submits to when they can make their next submission increases. Thus, students who start earlier are given more opportunities for automated feedback than a student who started closer to the deadline. The experiment discussed used TA-Bot over two semesters involving 144 students in CS1. When the dynamic rate limiting was enabled, students tended to start assignments earlier.

References

[1]
Jens Bennedsen and Michael Caspersen. 2007. Failure rates in introductory pro- gramming. SIGCSE Bulletin 39 (06 2007), 32--36. https://doi.org/10.1145/1272848.1272879
[2]
Jaime Spacco, Davide Fossati, John Stamper, and Kelly Rivers. 2013. Towards Improving Programming Habits to Create Better Computer Science Course Outcomes. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (Canterbury, England, UK) (ITiCSE '13). Association for Computing Machinery, New York, NY, USA, 243--248. https: //doi.org/10.1145/2462476.2465594

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cover image ACM Conferences
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2
March 2023
1481 pages
ISBN:9781450394338
DOI:10.1145/3545947
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 March 2023

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  1. automated assessment tools
  2. cs1
  3. gamification
  4. study behaviors
  5. unit testing

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  • National Science Foundation

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SIGCSE 2023
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Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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