Mining classroom observation data for understanding teacher's teaching modes
Zhang Hai, Luyao Yu, Yulu Cui, Mengxue Ji, Yining Wang
Abstract
The study of teacher development and teaching interaction in physical classrooms has presented research problems, but with the development of classroom observation and video analysis, quantitative and visual analysis of the teaching process has been realized. In order to better understand the teaching models of teachers, this study is devoted to mining classroom observation data. First, based on content analysis, a Dynamic Network Analysis of Classroom Teaching Elements (CTE-DNA) framework has been developed in which classroom observation data are divided into three dimensions, teaching behavior, instructional media, and technological pedagogical content knowledge (TPACK). Second, using the method of social network analysis, teacher's teaching models are analyzed by measuring adjacency matrix and relative centrality. Last, key nodes of the network as well as teaching models are visualized. Based on CTE-DNA, classroom observation data can be used for evaluating teacher's teaching behaviors and performance, and can be beneficial to teacher's professional development.