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Knowledge workers collaborative learning behavior modeling in an organizational social network

Computations related to learning processes within an organizational social network area require some network model preparation and specific algorithms in order to implement human behaviors in simulated environments. The proposals in this research model of collaborative learning in an organizational social network are based on knowledge resource distribution through the establishment of a knowledge flow. The nodes, which represent knowledge workers, contain information about workers social and cognitive abilities. Moreover, the workers are described by their set of competences, their skill level, and the collaborative learning behavior that can be detected through knowledge flow analysis. The proposed approach assumes that an increase in workers competence is a result of collaborative learning. In other words, collaborative learning can be analyzed as a process of knowledge flow that is being broadcast in a network. In order to create a more effective organizational social network for co-learning, the authors found the best strategies for knowledge facilitator, knowledge collector, and expert roles allocation. Special attention is paid to the process of knowledge flow in the community of practice. Acceleration within the community of practice happens when knowledge flows more effectively between community members. The presented procedure makes it possible to add new ties to the community of practice in order to influence community members competences. Both the proposed allocation and acceleration approaches were confirmed through simulations.