Social Network Analysis to Monitor Interactions in Virtual Learning Environment

Social network analysis is appointed as a promising approach to analyze the interactions in e-learning environments. Virtual Learning Environments (VLE) allow the register of significant amounts of data, proving to be a valuable data source to support researches on online interactions. This study is applied in a course offered in Brazil for training individuals to act as digital inclusion agents in telecenters. Our goals were to investigate the influence of the tutor's role in the grouping of individuals who interact through the VLE and to investigate the grouping of individuals from different regions of the country. Our approach presents the visualization of structure of interactions among the actors and discusses structural aspects of the interactions occurred in the VLE. We found that the classification of individuals by region could be predicted by the classification using a community detection method.