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