  In this paper, we investigate the opportunities of automating the judgment
process in online one-on-one math classes. We build a Wide & Deep framework to
learn fine-grained predictive representations from a limited amount of noisy
classroom conversation data that perform better student judgments. We conducted
experiments on the task of predicting students' levels of mastery of example
questions and the results demonstrate the superiority and availability of our
model in terms of various evaluation metrics.
