# Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing

arXiv: https://arxiv.org/abs/2107.06990 · HTML: https://mlsanity.com/paper/2107.06990/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication](https://mlsanity.com/paper/1405.1439/) (2014)
- [Annotation and Classification of Sentence-level Revision Improvement](https://mlsanity.com/paper/1909.05309/) (2019)
- [eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing](https://mlsanity.com/paper/1908.01992/) (2019)

## Built on by (most cited first, 9 of 9)

- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [ArgRewrite V.2: an Annotated Argumentative Revisions Corpus](https://mlsanity.com/paper/2206.01677/) (2022)
- [AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach](https://mlsanity.com/paper/2305.19902/) (2023)
- [eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback](https://mlsanity.com/paper/2501.00715/) (2025)
- [Predicting the Quality of Revisions in Argumentative Writing](https://mlsanity.com/paper/2306.00667/) (2023)
- [Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://mlsanity.com/paper/2302.05039/) (2023)
- [Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction](https://mlsanity.com/paper/2510.00268/) (2025)
- [Learning from Auxiliary Sources in Argumentative Revision Classification](https://mlsanity.com/paper/2309.07334/) (2023)
- [Intention-Adaptive LLM Fine-Tuning for Text Revision Generation](https://mlsanity.com/paper/2602.00477/) (2026)

## Abstract (arXiv)

Automated writing evaluation systems can improve students' writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the `RER' scheme) and apply it to 5th- and 6th-grade students' argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.