eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
August 06, 2019 ยท Declared Dead ยท ๐ AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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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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