Artificial Intelligence Based Behavior Sequential Analysis on the Teacher-Student Interaction Effect in Smart Classroom Environment

• In Event: Applications of Artificial Intelligence in Education

Abstract

The purpose of this study was to assess the effects of different types of teacher-student interaction in the smart classroom environment. Twenty classroom videos of teaching cases in a smart classroom environment were selected as research objects. Computer vision, one of the artificial intelligent technologies, was applied for video analysis. Cluster and lag sequential analyses combined with the Initiation-Response-Feedback theory were conducted to explore the teaching interaction patterns and assess the effects of teacher-student interaction in the smart classroom. The results indicated that teacher-student interaction can be divided into three types according to the behavior sequence. Among them, teacher-student interaction, which is composed of complex and multiple interaction patterns, is the most efficient.

Authors

• Kaiyue Lv, Capital Normal University

• Chin-Hsi Lin, The University of Hong Kong

• Rumeng Xu, Capital Normal University

• Zhong Sun, Capital Normal University