Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding
2026-03-25 • Computation and Language
Computation and LanguageComputers and Society
AI summaryⓘ
The authors studied how tutors and students interact during real math tutoring sessions by measuring how closely their conversation matches the problems and solutions being discussed. They used a method that compares the meaning of each part of the dialogue to the math problem and correct answers. Their analysis showed that tutors focus more on the problem at the start, while students' answers become more relevant as sessions go on. This shows scaffolding—the guidance tutors give—is a gradual process that depends on each person's role. The authors’ method can help better understand and evaluate tutoring conversations.
adaptive scaffoldingsemantic alignmentcosine similaritytutoring dialoguedialogue embeddingsmathematics educationmixed-effects modelsproblem-solvinginstructional dialogueconversational tutoring systems
Authors
Conrad Borchers, Jiayi Zhang, Ashish Gurung
Abstract
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.