Tutor Move Taxonomy: A Theory-Aligned Framework for Analyzing Instructional Moves in Tutoring

TL;DR

This paper introduces a comprehensive taxonomy for analyzing tutoring dialogue, enabling large-scale, systematic study of instructional moves to improve understanding and effectiveness of tutoring interactions.

Contribution

The paper presents a hybrid deductive-inductive taxonomy of tutoring moves, structured for scalable annotation and analysis of tutoring strategies in educational settings.

Findings

Developed a taxonomy with four main categories of tutoring behaviors.

Refined the taxonomy through iterative coding of authentic transcripts.

Enables AI-based annotation and empirical analysis of tutoring strategies.

Abstract

Understanding what makes tutoring effective requires methods for systematically analyzing tutors' instructional actions during learning interactions. This paper presents a tutor move taxonomy designed to support large-scale analysis of tutoring dialogue within the National Tutoring Observatory. The taxonomy provides a structured annotation framework for labeling tutors' instructional moves during one-on-one tutoring sessions. We developed the taxonomy through a hybrid deductive-inductive process. First, we synthesized research from cognitive science, the learning sciences, classroom discourse analysis, and intelligent tutoring systems to construct a preliminary framework of tutoring moves. We then refined the taxonomy through iterative coding of authentic tutoring transcripts conducted by expert annotators with extensive instructional and qualitative research experience. The resulting taxonomy organizes tutoring behaviors into four categories: tutoring support, learning support, social-emotional and motivational support, and logistical support. Learning support moves are further organized along a spectrum of student engagement, distinguishing between moves that elicit student reasoning and those that provide direct explanation or answers. By defining tutoring dialogue in terms of discrete instructional actions, the taxonomy enables scalable annotation using AI, computational modeling of tutoring strategies, and empirical analysis of how tutoring behaviors relate to learning outcomes.