Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 13 Jul 2022.
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References · 6
- 2017Attention Is All You Needcited by 59,932
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2016Wide & Deep Learning for Recommender Systemscited by 745
- 2020Neural Multi-Task Learning for Teacher Question Detection in Online Classroomscited by 6
- 2020Personalized Multimodal Feedback Generation in Educationcited by 4
- 2021Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Modelscited by 1
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