Knowledge workers collaborative learning behavior modeling in an organizational social network
Przemyslaw Rozewski, Jaroslaw Jankowski, Piotr Brodka, Radoslaw, Michalski

TL;DR
This paper models collaborative learning in organizational social networks by analyzing knowledge flow among workers, aiming to enhance co-learning strategies and community practice effectiveness through simulation-based validation.
Contribution
It introduces a novel model of collaborative learning based on knowledge flow analysis and proposes strategies for optimizing role allocation to improve community learning.
Findings
Effective knowledge flow strategies enhance community of practice.
Simulation confirms the impact of role allocation on learning acceleration.
Knowledge flow analysis correlates with increased worker competence.
Abstract
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…
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