🤖 AI Summary
This study addresses the lack of a systematic approach for analyzing how tutor behaviors influence learning outcomes in one-on-one tutoring. It proposes the first structured classification framework that categorizes tutoring behaviors into four support types, innovatively introducing a “student engagement spectrum” to differentiate between guided reasoning and direct explanation. Grounded in cognitive and learning sciences, the framework was iteratively refined through a hybrid deductive–inductive methodology applied to transcribed real-world tutoring dialogues. Designed to support both expert coding and large-scale structured annotation, this framework provides a scalable foundation for AI-driven automated behavior recognition, computational modeling, and empirical investigations into the relationship between instructional behaviors and learning gains.
📝 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.