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Evaluation of mathematical questioning strategies using data collected through weak supervision

2 December 2021
Debajyoti Datta
Maria Phillips
J. Bywater
Jennifer L. Chiu
G. Watson
Laura E. Barnes
Donald E. Brown
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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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