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

arXiv: https://arxiv.org/abs/2207.10645 · HTML: https://mlsanity.com/paper/2207.10645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms](https://mlsanity.com/paper/2005.07845/) (2020)
- [Personalized Multimodal Feedback Generation in Education](https://mlsanity.com/paper/2011.00192/) (2020)
- [Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models](https://mlsanity.com/paper/2107.07119/) (2021)

## 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.