Improving mathematical questioning in teacher training
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 2 Dec 2021.
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10 of 29 references.
References · 10
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- 2016Dialogue Learning With Human-In-The-Loopcited by 46
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