Open Access Article

Title: Data analysis of digital teaching resources and interactive behaviour between teachers and students based on K-means algorithm

Authors: Boren Gao; Zheng Chen

Addresses: School of Management, Guizhou University of Commerce, Guiyang 550014, Guizhou, China ' ShanXi Optical Storage Information Industry Development Ltd., Jinzhong 030600, Shanxi, China

Abstract: The focus of traditional research on teaching behaviour of teachers and students is mainly on identifying the expressions and behaviours of teachers and students, neglecting the analysis of interactive behaviour data between students and teachers. Effective recognition of teacher-student interaction behaviour through K-means algorithm, automatic recognition of classroom teacher-student behaviour using trained teacher and student behaviour recognition models, and analysis and statistics of paired and correlated teacher-student behaviour patterns in traditional classrooms through artificial intelligence technology, thereby promoting effective processing of teacher-student interaction behaviour data. Through experimental research, it has been verified that the method proposed in this article can accurately identify the interactive behaviour between teachers and students in smart teaching, effectively improving the effectiveness of teaching strategy formulation. Through research, it is known that combining intelligent algorithms for training and identifying teacher-student interaction behaviours is beneficial for improving educational and teaching modes, promoting teacher reflection, and promoting the development of educational and teaching informatisation reform towards a better path. In the future, continuous improvement can be made to the K-means algorithm to further promote the effective implementation of smart teaching.

Keywords: K-means algorithm; digitalisation; teaching resources; interactive behaviour.

DOI: 10.1504/IJICT.2024.139107

International Journal of Information and Communication Technology, 2024 Vol.24 No.7, pp.90 - 112

Received: 15 Jan 2024
Accepted: 16 Apr 2024

Published online: 13 Jun 2024 *