# Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms

arXiv: https://arxiv.org/abs/2005.07845 · HTML: https://mlsanity.com/paper/2005.07845/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 6 of 6)

- [The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education](https://mlsanity.com/paper/2404.02444/) (2024)
- [Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models](https://mlsanity.com/paper/2107.07119/) (2021)
- [DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments](https://mlsanity.com/paper/2206.12034/) (2022)
- [Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes](https://mlsanity.com/paper/2207.10645/) (2022)
- [Automatically Inferring Teachers' Geometric Content Knowledge: A Skills Based Approach](https://mlsanity.com/paper/2604.13666/) (2026)
- [AI-Driven Analytics of Team-Teaching Talk: Acoustic Patterns across Experience, Cohorts and the Learning Design](https://mlsanity.com/paper/2606.09831/) (2026)

## Abstract (arXiv)

Asking questions is one of the most crucial pedagogical techniques used by teachers in class. It not only offers open-ended discussions between teachers and students to exchange ideas but also provokes deeper student thought and critical analysis. Providing teachers with such pedagogical feedback will remarkably help teachers improve their overall teaching quality over time in classrooms. Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. Compared with traditional methods, our approach not only avoids cumbersome feature engineering, but also adapts to the task of multi-class question detection in real education scenarios. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions. We conducted extensive experiments on the question detection tasks in a real-world online classroom dataset and the results demonstrate the superiority of our model in terms of various evaluation metrics.