eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
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 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.
Cite
Text
Zhang et al. "eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019619Markdown
[Zhang et al. "eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/zhang2019aaai-erevise/) doi:10.1609/AAAI.V33I01.33019619BibTeX
@inproceedings{zhang2019aaai-erevise,
title = {{eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing}},
author = {Zhang, Haoran and Magooda, Ahmed and Litman, Diane J. and Correnti, Richard and Wang, Elaine and Matsumura, Lindsay Clare and Howe, Emily and Quintana, Rafael},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9619-9625},
doi = {10.1609/AAAI.V33I01.33019619},
url = {https://mlanthology.org/aaai/2019/zhang2019aaai-erevise/}
}