# eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

arXiv: https://arxiv.org/abs/1908.01992 · HTML: https://mlsanity.com/paper/1908.01992/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [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)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (2023)
- [How Well Can You Articulate that Idea? Insights from Automated Formative Assessment](https://mlsanity.com/paper/2404.11682/) (2024)

## Abstract (arXiv)

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.