Understanding Knowledge Convergence in a Cross-cultural Online Context: An Individual and Collective Approach

The concept of knowledge convergence refers to building a shared cognitive understanding among individuals through social interaction. It is considered as a crucial aspect of collaborative learning and plays a significant role in the process of consensus building. However, there is a lack of research exploring knowledge convergence in the context of online learning, especially in cross-cultural settings. Collaborative learning primarily focuses on constructing cognitive knowledge representations at the individual level, while online learning emphasizes the social mechanism of knowledge diffusion and flow at the collective level. This study aims to investigate individual online knowledge convergence through content analysis of social annotations within a cross-cultural course and using Simulation Investigation for Empirical Network Analysis (SIENA) to depict the collective social interaction. The findings reveal that online knowledge convergence exhibits distinct characteristics, quick consensus building could foster a harmonious community and similar experiences compensated for limited interactions, triggering deep consensus. Individual convergence leads to emergent properties such as reciprocity and transitivity within a dynamic collective interactive network, which can serve as novel indicators for evaluating knowledge convergence at the collective level. By approaching knowledge convergence from multifaceted perspectives, this study contributes to a comprehensive understanding of the concept across diverse learning contexts.