Abstract—Recently, the development of technology has
enriched the form of classroom interaction. Exploring the
characteristics of current classroom teaching interaction forms
can clarify the deficiencies of teaching interactions, thereby
improving teaching. Based on the existing classroom teaching
interactive coding system, this paper adopted ITIAS coding
system, and took classroom with interactive whiteboard,
interactive television or mobile terminals as research scene,
selected 20 classroom videos of teaching cases in this
environment as research objects. Computer vision, one of the
artificial intelligent technologies was applied for video analysis
from four aspects: the classroom teaching atmosphere, the
teacher-student interaction, the student-student interaction, the
interaction between human and technology. Through cluster
analysis, three clusters of sample’s behavioral sequences were
found. According to the analysis on the behavioral sequences
and the behavioral transition diagram of each cluster, three
classroom teaching interaction patterns were identified,
including immediate interaction pattern, waiting interaction
pattern and shallow interaction pattern.
Index Terms—Classroom interaction, artificial intelligent, interaction patterns, video analysis, lag sequential analysis.
The authors are with Capital Normal University, Beijing, China (corresponding author: Zhong Sun; e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
Cite: Kaiyue Lv, Zhong Sun, and Min Xu, "Artificial Intelligent Based Video Analysis on the Teaching Interaction Patterns in Classroom Environment," International Journal of Information and Education Technology vol. 11, no. 3, pp. 126-130, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).