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.

翻译：自适应辅导支架能促进学习，然而学术界在真实教学对话中尚缺乏稳健的衡量方法。随着远程人工辅导和大语言模型系统的兴起，这一研究空白愈发紧迫。我们提出一种基于嵌入的方法，通过对齐对话轮次、问题陈述与正确答案的语义来解析辅导支架的动态特征。具体而言，我们通过计算辅导者与学习者贡献内容及任务相关文本间的余弦相似度来操作化对齐过程。将该框架应用于Eedi问题锚定辅导对话数据集中的1,576段真实数学辅导对话后，分析揭示了任务对齐的系统性差异，以及参与者将自身贡献锚定于问题与解决方案内容时的差异化时间模式。进一步，混合效应模型表明：相较于消息顺序与长度等基线特征，角色特定语义对齐能显著预测辅导进程。辅导者的贡献在互动初期表现出更强的问题内容锚定性，而学习者的解决方案对齐程度与辅导进程呈适度正相关。这些发现表明辅导支架是一种持续性的、对角色敏感的任务语义锚定过程。通过捕捉随时间变化的角色特定对齐特征，本方法为分析教学对话与评估对话型辅导系统提供了原理性框架。