Examining the dynamics of knowledge convergence in online learning context: A network perspective

Abstract

Knowledge convergence, originating from computer-supported collaborative learning (CSCL), is often defined as building a shared cognitive understanding through social interactions. With an increasing focus on large-scale collaboration and online learning in CSCL, it is crucial to examine how knowledge convergence occurs in online settings. This study investigates how learners develop cognitive consensus in online discussions and assess how social interactions and learners' role influence these dynamics in a MOOC using video-based social annotation. Mixed-methods, including Epistemic Network Analysis (ENA), Simulation Investigation for Empirical Network Analysis (SIENA), and role trajectory clustering were employed. The findings suggest that cognitive consensus in discussions originates from sharing similar experiences and evolves into more advanced levels over time. Reciprocity and transitivity are crucial for establishing network cohesion while achieving cognitive consensus. Learners with similar role trajectories tend to interact together. This study expands the traditional CSCL paradigm by examining how social interactions shape discussion network dynamics and how learners’ role trajectories influence these dynamics. We argue that network cohesiveness should be included in the framework of online knowledge convergence, alongside cognitive consensus. Dynamic network analysis is essential for understanding the complex mechanisms driving online knowledge convergence occurring, where the cognitive and social attributes of learning are interwoven.