Related papers: Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes

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

URL: http://arxiv.org/abs/2207.10645v1
Date: Wed, 13 Jul 2022 01:38:57 GMT
Title: Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes
Authors: Jiahao Chen, Zitao Liu, Weiqi Luo
Abstract summary: We build a framework to learn fine-grained predictive representations from a limited amount of noisy classroom conversation data. We conducted experiments on the task of predicting students' levels of mastery of example questions.
Score: 27.07952179997629
License: http://creativecommons.org/licenses/by/4.0/
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.

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