Academic

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

arXiv:2603.05778v1 Announce Type: new 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 categ

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Zhuqian Zhou, Kirk Vanacore, Tamisha Thompson, Jennifer St John, Rene Kizilcec · · 1 min read · 9 views
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arXiv:2603.05778v1 Announce Type: new 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.

Executive Summary

This article presents a tutor move taxonomy, a structured annotation framework for systematically analyzing tutors' instructional actions during one-on-one tutoring sessions. The taxonomy was developed through a hybrid deductive-inductive process, combining research from cognitive science, learning sciences, classroom discourse analysis, and intelligent tutoring systems. The resulting framework organizes tutoring behaviors into four categories: tutoring support, learning support, social-emotional and motivational support, and logistical support. The taxonomy enables scalable annotation using AI, computational modeling of tutoring strategies, and empirical analysis of how tutoring behaviors relate to learning outcomes. This framework has significant implications for the National Tutoring Observatory and may contribute to the development of more effective tutoring practices.

Key Points

  • ▸ The tutor move taxonomy is a structured annotation framework for analyzing tutors' instructional actions during one-on-one tutoring sessions.
  • ▸ The taxonomy was developed through a hybrid deductive-inductive process combining research from multiple disciplines.
  • ▸ The framework organizes tutoring behaviors into four categories, with learning support moves further distinguished along a spectrum of student engagement.

Merits

Strength

The taxonomy provides a systematic and structured approach to analyzing tutoring behaviors, enabling scalable annotation and computational modeling of tutoring strategies.

Demerits

Limitation

The taxonomy may require significant training and expertise for annotators to accurately label tutors' instructional moves, which may limit its widespread adoption.

Expert Commentary

The tutor move taxonomy presents a significant contribution to the field of educational research, providing a systematic and structured approach to analyzing tutoring behaviors. The taxonomy's development through a hybrid deductive-inductive process demonstrates a commitment to rigor and validity, ensuring that the framework is grounded in a deep understanding of the theoretical underpinnings of tutoring. However, the taxonomy may require significant training and expertise for annotators to accurately label tutors' instructional moves, which may limit its widespread adoption. Nevertheless, the taxonomy has significant implications for the development of more effective intelligent tutoring systems, learning analytics, and pedagogical strategies.

Recommendations

  • ✓ Further research is needed to refine the taxonomy and ensure its reliability and validity across different contexts and populations.
  • ✓ The development of training programs and resources for annotators is essential to ensure that the taxonomy is used effectively and accurately.

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