A Research on Online Teaching Behavior of Chinese Local University Teachers Based on Cluster Analysis

COVID-19 boosted online teaching and yielded a significant amount of valuable data, yet utilizing it for education is challenge. This study employed the K-means clustering method to analyze behavior data 1147 courses from local university in East China. As result, five types with distinct behaviors were identified: resource preparation (4.1%), classroom interaction (3.6%), task evaluation (9.2%), active (15.5%), inactive (67.6%). By examining relationship between these course academic performance, authors discovered no difference performance students three groups (i.e., preparation, interaction, evaluation) group. However, there was disparity courses. These findings can assist teachers planning activities more effectively improving outcomes.