Improving mathematical questioning in teacher training
This work addresses a specific need in teacher training for improving mathematical questioning skills, but it is incremental as it builds on existing methods and frameworks.
The paper tackled the problem of modeling open-ended mathematical questioning in teacher training by building a text-based conversational agent, achieving high conversation success rate and user satisfaction.
High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, dialogue-oriented open-ended conversations such as teaching a student about scale factors can be difficult to model. This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills based on the well-known Instructional Quality Assessment. We take a human-centered approach to designing our system, relying on advances in deep learning, uncertainty quantification, and natural language processing while acknowledging the limitations of conversational agents for specific pedagogical needs. Using experts' input directly during the simulation, we demonstrate how conversation success rate and high user satisfaction can be achieved.