Predicting the Quality of Revisions in Argumentative Writing
This work addresses the challenge of assessing revision quality in argumentative writing for students, though it is incremental as it builds on existing methods with a novel prompting approach.
The paper tackled the problem of predicting the quality of argument revisions in student writing by using Chain-of-Thought prompts to generate argument contexts for ChatGPT, achieving superior performance over baselines on elementary and college essay corpora.
The ability to revise in response to feedback is critical to students' writing success. In the case of argument writing in specific, identifying whether an argument revision (AR) is successful or not is a complex problem because AR quality is dependent on the overall content of an argument. For example, adding the same evidence sentence could strengthen or weaken existing claims in different argument contexts (ACs). To address this issue we developed Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality predictions. The experiments on two corpora, our annotated elementary essays and existing college essays benchmark, demonstrate the superiority of the proposed ACs over baselines.