The Virtual Community: Homesteading on the Electronic Frontier
The Virtual Community: Homesteading on the Electronic Frontier
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A Relational View of Information Seeking and Learning in Social Networks
Management Science
Measuring performance & interrelatedness in social networks of knowledge workers
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Predicting students' final performance from participation in on-line discussion forums
Computers & Education
| Hi-index | 0.00 |
Traditional models of learning using a sociological perspective include social learning, situated learning and models of connectivisim and self-efficacy. While these models explain how individuals learn in varying social dimensions, very few studies provide empirical validation of such models and extend them to include group learning and performance. In this exploratory study, we develop a theoretical model based on social learning and social network theories to understand how knowledge professionals engage in learning and performance, both as individuals and as groups. We investigate the association between egocentric network properties (structure, position and tie), 'content richness' in the social learning process and performance. Analysis from data collected using an online eLearning environment shows that rather than performance; social learning is influenced by properties of social network structure (density, inter-group and intra-network communication), relations (tie strength) and position (efficiency). Furthermore, individuals who communicate with others internal rather than external to the group show higher tendencies of social learning. The contribution of this study is therefore two-fold: a theoretical development of a social learning and networks based model for understanding learning and performance; and the construction of a novel metric called 'content richness' as a surrogate measure for social learning. In conclusion, a useful implication of the study is that the model fosters understanding social factors that influence learning and performance in the domain of learning analytics. It also begs the question of whether the relationship between social networks and performance is mediated or moderated by learning and whether assumptions of the model hold true in non-educational domains.