Identifying Learning Leaders in Online Social Networks Based on Community of Practice Theoretical Framework and Information Entropy

The dynamic shifts in educational settings have led scholars to explore online social networks (OSNs) as emergent environment for learning communities. Recognized for their effectiveness in fostering lifelong learning, these online groups surpass traditional educational methods in scope and impact. However, they require meticulous management to maintain efficacy and facilitate knowledge sharing. Addressing this need, the current study introduces the objective of identifying and defining learning leaders to aid in the management of these learning communities. To achieve this, two innovative metrics have been developed, rooted in the theoretical framework of Community of Practice (CoP) and the principles of information entropy: the Learning Leader Metric (LLM) and the Weighted Learning Leader Metric (WLLM). The LLM evaluates three key areas: Learning Engagement, Expertise, and Domain Relevance, forming a comprehensive tripartite framework. In contrast, the WLLM enhances this evaluation by applying information entropy to assign varying weights to these dimensions, thus refining the assessment process while preserving computational efficiency. These metrics operate in an unsupervised manner, addressing the challenge of the absence of annotated data which is a common hurdle in supervised learning scenarios, particularly in information retrieval tasks. Tested against five established expert finding models, the empirical results of this study confirm that both LLM and WLLM demonstrate superior effectiveness in accurately identifying highly relevant experts compared to other models considered in the analysis.