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

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arxiv 1908.01992 v1 pith:CN3QWL6N submitted 2019-08-06 cs.CL cs.AI

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 Wang , Lindsay Clare Matsumura , Emily Howe , Rafael Quintana This is my paper
classification cs.CL cs.AI
keywords writingstudentsereviseevidencefeedbackformativetextessay
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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