Evaluation of mathematical questioning strategies using data collected through weak supervision

• URL: http://arxiv.org/abs/2112.00985v1
• Date: Thu, 2 Dec 2021 05:12:36 GMT
• Title: Evaluation of mathematical questioning strategies using data collected through weak supervision
• Authors: Debajyoti Datta, Maria Phillips, James P Bywater, Jennifer Chiu, Ginger S. Watson, Laura E. Barnes, Donald E Brown
• Abstract summary: This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills. Using a human-in-the-loop approach, we collected a high-quality training dataset for a mathematical questioning scenario.
• Score: 1.794107419334178
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A large body of research demonstrates how teachers' questioning strategies can improve student learning outcomes. However, developing new scenarios is challenging because of the lack of training data for a specific scenario and the costs associated with labeling. This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills. Using a human-in-the-loop approach, we collected a high-quality training dataset for a mathematical questioning scenario. Using recent advances in uncertainty quantification, we evaluated our conversational agent for usability and analyzed the practicality of incorporating a human-in-the-loop approach for data collection and system evaluation for a mathematical questioning scenario.

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