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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 . Proceedings of the AAAI Conference on Artificial Intelligence 2019 – 40 citations

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AAAI Compositional Generalization Content Enrichment Image Text Integration Interactive Environments Interdisciplinary Approaches Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering

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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