  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.
