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

August 06, 2019 ยท Declared Dead ยท ๐Ÿ› AAAI Conference on Artificial Intelligence

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Authors Haoran Zhang, Ahmed Magooda, Diane Litman, Richard Correnti, Elaine Wang, Lindsay Clare Matsumura, Emily Howe, Rafael Quintana arXiv ID 1908.01992 Category cs.CL: Computation & Language Cross-listed cs.AI Citations 41 Venue AAAI Conference on Artificial Intelligence Last Checked 4 months ago
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.
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