  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.
