Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.
@article{arxiv.1909.05309,
title = {Annotation and Classification of Sentence-level Revision Improvement},
author = {Tazin Afrin and Diane Litman},
journal= {arXiv preprint arXiv:1909.05309},
year = {2019}
}