Improving mathematical questioning in teacher training
- URL: http://arxiv.org/abs/2112.01537v2
- Date: Mon, 6 Dec 2021 10:49:01 GMT
- Title: Improving mathematical questioning in teacher training
- Authors: Debajyoti Datta, Maria Phillips, James P Bywater, Jennifer Chiu,
Ginger S. Watson, Laura E. Barnes, Donald E Brown
- Abstract summary: High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies.
This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills.
- Score: 1.794107419334178
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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