Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes
2022-07-13Code Available0· sign in to hype
Jiahao Chen, Zitao Liu, Weiqi Luo
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Abstract
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.