作者
Evan Zheran Liu, David Yuan, Ahmed Ahmed, Elyse Cornwall, Juliette Woodrow, Kaylee Burns, Allen Nie, Emma Brunskill, Chris Piech, Chelsea Finn
发表日期
2024/3/7
图书
Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
页码范围
736-742
简介
In this paper, we detail the successful deployment of a machine learning autograder that significantly decreases the grading labor required in the Breakout computer science assignment. This assignment - which tasks students with programming a game consisting of a controllable paddle and a ball that bounces off the paddle to break bricks - is popular for engaging students with introductory computer science concepts, but creates a large grading burden. Due to the game's interactive nature, grading defies traditional unit tests and instead typically requires 8+ minutes of manually playing each student's game to search for bugs. This amounts to 45+ hours of grading in a standard course offering and prevents further widespread adoption of the assignment. Our autograder alleviates this burden by playing each student's game with a reinforcement learning agent and providing videos of discovered bugs to instructors. In …
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EZ Liu, D Yuan, A Ahmed, E Cornwall, J Woodrow… - Proceedings of the 55th ACM Technical Symposium on …, 2024