Designs for Visualizing Collective Intelligence in Knowledge Building Communities

Abstract

Collective intelligence [1, 2] is a key characteristic of innovative groups and organizations that self-organize in online environments to generate and disseminate knowledge for public good. Collaborative Innovation Networks (COINs) [3] and Knowledge Building communities [4] represent powerful models that inform the emergence and sustainability of collective intelligence in organizational contexts, as well as educational contexts. For example, a key dynamic of COINs and Knowledge Building communities is collective responsibility for advancing the state of community knowledge [5], as indicated by decentralized social structures and rotating leadership over time [6]. Additionally, both COINs and Knowledge Building communities engage in knowledge practices that leverage the affordances of online environments, using embedded data analytics to visualize group dynamics, troubleshoot emergent issues, and identify promising areas for improvement. This study highlights the theoretical intersections between these two models of organizational innovation in order to explore technological designs for visualizing collective intelligence in Knowledge Forum [7], an online platform optimized for knowledge creation and innovation. More specifically, the six indicators of COINs [8] - rapid response, balanced contribution, central leaders, rotating leadership, honest sentiment, and innovative language - are refined and adapted within the context of Knowledge Building communities, wherein the design challenge is enabling self-organization around idea improvement. Using social network analysis, lexical analysis, sentiment analysis, and statistical analysis, next-generation designs for Knowledge Forum analytics are proposed. The educational value and practical implications of these designs are discussed within the context of education for innovation in technology-based societies.