  We develop models to classify desirable evidence and desirable reasoning
revisions in student argumentative writing. We explore two ways to improve
classifier performance - using the essay context of the revision, and using the
feedback students received before the revision. We perform both intrinsic and
extrinsic evaluation for each of our models and report a qualitative analysis.
Our results show that while a model using feedback information improves over a
baseline model, models utilizing context - either alone or with feedback - are
the most successful in identifying desirable revisions.
