Wide Deep Learning for Judging Student Performance in Online One-on-one Math Classes
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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.
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