Auditing an Automatic Grading Model with deep Reinforcement Learning
- URL: http://arxiv.org/abs/2405.07087v1
- Date: Sat, 11 May 2024 20:07:09 GMT
- Title: Auditing an Automatic Grading Model with deep Reinforcement Learning
- Authors: Aubrey Condor, Zachary Pardos,
- Abstract summary: We explore the use of deep reinforcement learning to audit an automatic short answer grading (ASAG) model.
We show that a high level of agreement to human ratings does not give sufficient evidence that an ASAG model is infallible.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We explore the use of deep reinforcement learning to audit an automatic short answer grading (ASAG) model. Automatic grading may decrease the time burden of rating open-ended items for educators, but a lack of robust evaluation methods for these models can result in uncertainty of their quality. Current state-of-the-art ASAG models are configured to match human ratings from a training set, and researchers typically assess their quality with accuracy metrics that signify agreement between model and human scores. In this paper, we show that a high level of agreement to human ratings does not give sufficient evidence that an ASAG model is infallible. We train a reinforcement learning agent to revise student responses with the objective of achieving a high rating from an automatic grading model in the least number of revisions. By analyzing the agent's revised responses that achieve a high grade from the ASAG model but would not be considered a high scoring responses according to a scoring rubric, we discover ways in which the automated grader can be exploited, exposing shortcomings in the grading model.
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