基于表示学习研究教程支架的动态过程
Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding
AI总结：
本文通过对话语义对齐分析，揭示了教程支架中任务对齐和时间模式的系统差异，证明角色特定语义对齐能预测教学进展。
摘要：
适应性支架能增强学习，但该领域缺乏在真实辅导对话中衡量其有效性的稳健方法。本文提出基于嵌入的方法，通过对齐对话回合、问题陈述和正确解的语义来分析支架动态。具体而言，通过计算辅导者与学习者贡献及任务相关内容之间的余弦相似度来实现对齐。该框架应用于1576个真实的数学辅导对话数据集。分析揭示了任务对齐的系统差异和参与者在问题和解内容中接地的时序模式。进一步，混合效应模型显示，角色特定语义对齐能超越基线特征（如消息顺序和长度）预测教程进展。辅导者的贡献在早期互动中更紧密地接地于问题内容，而学习者解的对齐则与进展呈适度正相关。这些发现支持支架作为连续、角色敏感的过程，基于任务语义。通过捕捉角色特定的对齐过程，该方法为分析教学对话和评估对话辅导系统提供了原理性方法。
英文摘要：
Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue. This gap has become more pressing with the rise of remote human tutoring and large language model-based systems. We introduce an embedding-based approach that analyzes scaffolding dynamics by aligning the semantics of dialogue turns, problem statements, and correct solutions. Specifically, we operationalize alignment by computing cosine similarity between tutor and student contributions and task-relevant content. We apply this framework to 1,576 real-world mathematics tutoring dialogues from the Eedi Question Anchored Tutoring Dialogues dataset. The analysis reveals systematic differences in task alignment and distinct temporal patterns in how participants ground their contributions in problem and solution content. Further, mixed-effects models show that role-specific semantic alignment predicts tutorial progression beyond baseline features such as message order and length. Tutor contributions exhibited stronger grounding in problem content early in interactions. In contrast, student solution alignment was modestly positively associated with progression. These findings support scaffolding as a continuous, role-sensitive process grounded in task semantics. By capturing role-specific alignment over time, this approach provides a principled method for analyzing instructional dialogue and evaluating conversational tutoring systems.
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。
