Adapting to Complexity: Teacher-Student interactions in synchronous online language classes through a dynamic systems lens

Abstract

Teacher-student interaction is widely recognized as critical to the success of language learning. However, there remains a significant gap in understanding these interactions from a non-linear systems perspective, particularly in online learning environments. This study applies Complex Dynamic Systems Theory (CDST) to investigate the co-adaptive patterns of teacher-student exchanges in online Chinese language classes. By examining real-time interactions and their dynamic nature, we analyzed how interactional patterns evolved and shaped the learning process, using a visualized coding scheme to focus on variations within and between teachers. The study involved 23 adult students studying Chinese as a second language in a Chinese university, with data collected from 12 lessons across a semester. Our analysis identified predominant patterns, such as a frequent reliance on closed questions followed by short responses. While students adjusted their answers based on question complexity, teachers often did not, revealing a lack of adaptability in their questioning techniques. The study calls for further exploration and improved training in adaptive questioning strategies, as this remains a challenge across both online and offline settings. These findings highlight the need for flexible and responsive teaching to meet the increasing demands of dynamic online learning environments.