Improving mathematical questioning in teacher training

Autor:	Datta, Debajyoti, Phillips, Maria, Bywater, James P, Chiu, Jennifer, Watson, Ginger S., Barnes, Laura E., Brown, Donald E
Rok vydání:	2021
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to appear at the NeurIPS 2021 Human Centered AI Workshop (HCAI). Data collection process for this data is described here arXiv:2112.00985
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.01537 Zobrazit plný text záznamu View this record from Arxiv