eRevise

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

Haoran Zhang, Ahmed Magooda, Diane Litman, Richard Correnti, Elaine Lin Wang, Lindsay Clare Matsumura, Emily Howe, Rafael Quintana

ResearchPosted on rand.org Feb 26, 2020Published in: Proceedings of the AAAI Conference on Artificial Intelligence (2019). doi: 10.1609/aaai.v33i01.33019619

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 rubricbased 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.

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Document Details

  • Availability: Non-RAND
  • Year: 2019
  • Pages: 7
  • Document Number: EP-68093

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