Analyzing Teacher-Student Interaction Patterns Through Deep Learning: Implications for Classroom Management and Teaching Effectiveness

Abstract This study employed a multimodal deep learning architecture to analyze teacher-student interaction patterns across 432 class hours in diverse educational settings. The model successfully identified five distinct interaction patterns with 87.6% accuracy, revealing significant correlations between specific interaction characteristics and educational outcomes. Collaborative scaffolding demonstrated strong positive associations with student engagement (r = 0.78) and learning interest (β = 0.73), while Socratic questioning significantly impacted ability development (β = 0.68). Analysis revealed that effective teachers strategically orchestrate interaction sequences in response to specific learning objectives rather than maximizing any single interaction type. The findings challenge traditional classroom management assumptions and offer data-driven insights for enhancing teaching effectiveness through intentional interaction pattern deployment. This research bridges educational theory with computational modeling, providing a methodological framework for quantifying classroom dynamics through multimodal interaction analysis.