eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
This addresses the challenge of helping elementary and middle school students improve essay writing through automated feedback, though it is incremental as it builds on existing rubric-based scoring methods.
The paper tackled the problem of providing formative feedback on text evidence usage in student writing by developing eRevise, a web-based tool using natural language processing; in a pilot with 7 classrooms in grades 5-6, it improved the quality of text evidence usage after students received feedback and revised their drafts.
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.