An approach to a graph-based model for encouraging transfer of knowledge in learning activities

Abstract

Knowledge transfer is a key aspect of securing the continuous improvement of learning, being the reuse and recombination of knowledge a difficult task. Hence, this process can be enriched by approaches such as graph theory, transactive memory and social networks to boost and facilitate the team members' learning. This paper presents an approaching of representing knowledge transfer of learning contexts, applying graph structures to perceive dynamically the collaborative learning under transactive memory mechanisms and social network aspects when connecting participants in building knowledge. The proposed model allows rate the learning process and determines weak elements to so reinforcing the understanding and comprehension of a determined subject. Through case studies, this approach was validated by people belonging to academic and business context, which participated in learning collaboratively specific topics and provided support for acquiring skills. The dynamic of the model, supported by a graph structure was analysed to disclose encouraging perceptions on this proposal.