迈向可操作的教学反馈：数学教学与辅导对话的多视角分析
Towards Actionable Pedagogical Feedback: A Multi-Perspective Analysis of Mathematics Teaching and Tutoring Dialogue
AI总结：
针对数学教学与辅导对话中话语多功能性和话语遗漏问题，提出融合谈话动作、对话行为与话语关系的多视角分析框架，揭示无谈话动作话语的关键作用，助力AI辅助教育反馈。
摘要：
有效的反馈对于改进数学教育中的教学实践至关重要，研究人员常常借助先进的自然语言处理（NLP）模型，从多个视角分析课堂对话。然而，话语级（utterance-level）话语分析面临两大主要挑战：（1）多功能性，即单个话语可能服务于多种目的，单一标签无法捕捉其全部功能；（2）许多话语被排除在特定领域的话语动作（discourse move）分类之外，导致它们在反馈中被遗漏。为应对这些挑战，我们提出了一种多视角话语分析方法，将特定领域的谈话动作（talk moves）与对话行为（采用包含43个标签的扁平化多功能SWBD-MASL模式）以及话语关系（应用包含16种关系的分段话语表征理论）相结合。我们的自上而下分析框架能够全面理解包含谈话动作的话语以及不包含谈话动作的话语。该方法被应用于两个数学教育数据集：TalkMoves（教学）和SAGA22（辅导）。通过分布性一元分析、序列谈话动作分析以及多视角深度剖析，我们发现了有意义的话语模式，并揭示了不包含谈话动作的话语的重要作用，表明这些话语远非单纯的填充物，而是在引导、确认和构建课堂话语中发挥着关键功能。这些见解强调了将话语关系和对话行为纳入人工智能辅助教育系统以增强反馈并创造更具响应性的学习环境的重要性。我们的框架可能有助于提供人类教育者的反馈，同时也有助于开发能够有效模仿教育者和学生角色的AI智能体。
英文摘要：
Effective feedback is essential for refining instructional practices in mathematics education, and researchers often turn to advanced natural language processing (NLP) models to analyze classroom dialogues from multiple perspectives. However, utterance-level discourse analysis encounters two primary challenges: (1) multifunctionality, where a single utterance may serve multiple purposes that a single tag cannot capture, and (2) the exclusion of many utterances from domain-specific discourse move classifications, leading to their omission in feedback. To address these challenges, we proposed a multi-perspective discourse analysis that integrates domain-specific talk moves with dialogue act (using the flattened multi-functional SWBD-MASL schema with 43 tags) and discourse relation (applying Segmented Discourse Representation Theory with 16 relations). Our top-down analysis framework enables a comprehensive understanding of utterances that contain talk moves, as well as utterances that do not contain talk moves. This is applied to two mathematics education datasets: TalkMoves (teaching) and SAGA22 (tutoring). Through distributional unigram analysis, sequential talk move analysis, and multi-view deep dive, we discovered meaningful discourse patterns, and revealed the vital role of utterances without talk moves, demonstrating that these utterances, far from being mere fillers, serve crucial functions in guiding, acknowledging, and structuring classroom discourse. These insights underscore the importance of incorporating discourse relations and dialogue acts into AI-assisted education systems to enhance feedback and create more responsive learning environments. Our framework may prove helpful for providing human educator feedback, but also aiding in the development of AI agents that can effectively emulate the roles of both educators and students.
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。
