利用弱监督收集的数据评价数学提问策略
Evaluation of mathematical questioning strategies using data collected through weak supervision
摘要：
大量的研究表明，教师的提问策略可以提高学生的学习效果。然而，由于缺乏特定场景的训练数据以及与标记相关的成本，开发新场景具有挑战性。本文提出了一个高保真的，基于人工智能的课堂模拟器，以帮助教师排练基于研究的数学提问技能。使用人在回路的方法，我们收集了一个高质量的数学提问场景的训练数据集。使用最新进展的不确定性量化，我们评估了我们的会话代理的可用性和分析的实用性，将人在回路中的方法，数据汇集和系统评估的数学提问的情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
