Towards a Social Networks Model for Online Learning & Performance

In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we refer to its structural, position and relationship attributes. Analysis of data collected from an e-learning environment shows that rather than performance, social learning correlates with properties of social networks: (i) structure (density, inter-group and intra-network communication) and (ii) position (efficiency), and (iii) relationship (tie strength). In particular, individuals who communicate with internal group members rather than external members express higher tendencies of "content richness" in social learning. The contribution of this study is three-fold: (i) a theoretical development of a social network based model for understanding learning and performance, which addresses the lack of empirical validation of current models in social learning; (ii) the construction of a novel metric called "content richness" as a surrogate indicator of social learning; and (iii) demonstration of how the use of social network analysis and computational text-mining approaches can be used to operationalize the model for studying learning and performance. In conclusion, a useful implication of the study is that the model fosters understanding social factors that influence learning and performance in project management. The study concludes that associations between social network properties and the extent to which interactions are " content-rich" in eLearning domains cannot be discounted in the learning process and must therefore be accounted for in the organizational learning design.