Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
This work addresses the need for pedagogical feedback to improve teaching quality in online education, though it is incremental as it builds on existing neural and multi-task learning methods.
The authors tackled the problem of automatically detecting teacher questions from audio recordings in online classrooms, achieving superior performance on a real-world dataset across multiple evaluation metrics.
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.