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

## TL;DR

eRevise is a web-based tool that leverages natural language processing to give formative feedback on students' use of evidence in essays, improving writing quality through targeted revision support.

## Contribution

This paper introduces eRevise, a novel NLP-enabled environment that provides rubric-based formative feedback to enhance students' evidence use in writing.

## Key findings

- Improved quality of text evidence usage after feedback
- Effective in grades 5 and 6 classrooms
- Supports rubric-based formative assessment

## Abstract

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.