Artificial Intelligent Based Video Analysis on the Teaching Interaction Patterns in Classroom Environment

Recently, the development of technology has enriched form classroom interaction. Exploring characteristics current teaching interaction forms can clarify deficiencies interactions, thereby improving teaching. Based on existing interactive coding system, this paper adopted ITIAS and took with whiteboard, television or mobile terminals as research scene, selected 20 videos cases in environment objects. Computer vision, one artificial intelligent technologies was applied for video analysis from four aspects: atmosphere, teacher-student interaction, student-student between human technology. Through cluster analysis, three clusters sample’s behavioral sequences were found. According to transition diagram each cluster, patterns identified, including immediate pattern, waiting pattern shallow pattern.