How to analyse a semantic social network of learners in a social learning environment?

Abstract

Interactions among learners in e-learning systems enhance the learning process and cognitive level. To understand the learners' socialisation, the learners' social network analysis can provide more significant answers based on realistic models beyond topological representations. In this paper, we show how to extract semantic properties from learner-learner interactions, how to model and analyse a semantic social network of learners. We build an experimental prototype able to exploit efficiently the interaction semantics and parameterise the analysis. We reveal leadership roles dominating the network, if a learner holds a strategic position and to what extent his profile and affiliation are semantically affected by the collaborative tools. Findings show that the learner's potentiality and the network connectivity change following different viewpoints, the type of links and the positivity to interact. The collectivity spirit inside a learning community is also shown semantically characterised and contributed by closer learners sharing the same relationship type.