From Tutor Moves to Tutoring States: Modeling the Timing and Sequencing of Pedagogical Strategies for Student Engagement

Understanding how tutoring unfolds requires capturing not only what tutors and students say, but also the multi-turn pedagogical strategies that structure their conversation. In this study, we analyze 77 online math tutoring sessions (about 40 hours of tutoring) using a combination of LLM-assisted annotation and statistical modeling. Building on utterance-level annotations with a taxonomy of tutor moves, we identify three recurrent latent states using a Hidden Markov Model: Inquiry Elicitation (dominated by prompting and probing student reasoning), Direct Instruction (centered on explanation and scaffolding), and Affective Support (characterized by praise and socioemotional support). Sequential pattern mining revealed that these states exhibit distinct instructional motifs—for example, Inquiry Elicitation involves repeated prompting, while Direct Instruction features sustained explanatory scaffolding. These dynamics were correlated with different levels of student engagement. Sessions starting with Inquiry Elicitation and ending with Affective Support elicited more student talk, while sustaining Direct Instruction was associated with a higher likelihood that students met tutoring dosage recommendations by returning for additional sessions. Together, these results suggest that the impact of tutoring may lie not just in which strategies tutors employ, but in how those strategies are ordered and coordinated over time—patterns that reveal the deeper conversational architecture shaping student engagement. More broadly, this work demonstrates how integrating generative AI annotation with advanced statistical modeling can scale analyses of tutoring dialogue and identify conversational practices that may prompt sustained engagement.