Representation Learning to Study Temporal Dynamics in Tutorial Scaffolding

TL;DR

This paper presents an embedding-based method to analyze the temporal dynamics of scaffolding in tutoring dialogues, revealing role-specific semantic alignment patterns that predict tutorial progression.

Contribution

It introduces a novel semantic alignment framework for measuring scaffolding dynamics in real-world tutoring dialogues, applicable to both human and AI tutoring systems.

Findings

Role-specific semantic alignment predicts tutorial progression.

Tutor grounding in problem content is stronger early in interactions.

Student solution alignment correlates with progression.

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.