Following the previous post…
The structural paradigm of Social Network Analysis (SNA) with its constitutive theory and methods, began to emerge around the 1930s, applied and influenced by a broad range of disciplines such as sociology, psychology and statistics (Scott and Carrington, 2011).
In social network theory a social structure is represented by a group of “social actors” connected by a set of relationships. These actors – or “nodes” – can be individuals, groups, institutions, organisations or even Web pages. There can be many different kinds of relationships – or “ties” – between nodes, which constitute a “map” of connections between the actors in a network. When a social network is visualised the nodes are usually represented by points and the ties by lines linking one or more nodes.
The focus of SNA is on the relationships between nodes and the structure of these connections. The object of study is the pattern, nature and dynamics of these interactions, as opposite to the individual characteristics of the actors. This representation allows analysis of the social processes determined by the relationships between the individuals (Martino and Spoto, 2006). SNA enables to visualise the position of a social agent within a particular network, however, because less importance is given to individuals, this theory has less consideration for the influence of personal characteristics and individual agency in determining the success of a relationship.
The connections within nodes in a network facilitate exchange of “resources” which can be influenced by the quantity and quality of the linkages and interactions. Looking at online educational networks through a SNA lens is a way to establish wether the ways in which individuals connect with a particular environment may influence their access to information and knowledge. As Rita Kop states “the Web is portrayed as a democratic network on which peer to peer interaction might lead to a creative explosion and participative culture of activity” (Kop, 2012 p3) but how is this potential being exploited in education? What are the processes beyond this interaction and how can they be used to facilitate students access to information, knowledge and ideas?
The potential of social media in forming networks, extending students knowledge and translating this into academic achievement is impacted by a multitude of elements such as individuals’ attitudes (Morrison, 2002), University environment and socialisation processes (Yu et al., 2010). Other mechanisms influencing this process may be the particular educational practices and experiences, the success of connections, the dynamics in which participants negotiate the structure of the network and exchange practices and many others which can not be controlled.
This analysis can be enriched by Bordieau’s concept of “social capital”, which introduces a set of dynamics between the social dimension, the identity dimension (habitus) and the individual’s practice. In this system of reciprocal influences it is interesting to look at the transformation processes and effects of elements such as “weak ties”, “brokers”, “latent connections” and “structural holes” in the information flow within a network.
Acknowledging the potential of these processes and of the structure of a network is vital for educators who aim to harness the changing affordances of Web 2.0 technology applied to pedagogical interventions. According to Morrison (2002) the configuration of the network structure has an important role on the learning processes occurring during socialisation (p 1157). This is confirmed by a recent study on student engagement via social network where Badge, Saunders and Cann (2012) suggest that “where online communication channels are adopted, teaching staff need to ensure they have adequate network connections with all students, but especially to cultivate connections with and the networks of lower-performers” (p11). Student learning is influenced by the quantity and quality of connections in a network and by the students’ position in the network, which is determined by both giving and getting information from other student (Hommes, 2012).
Badge, J.L., Saunders, N.F.W., Cann, A.J. (2012). Beyond marks: new tools to visualise student engagement via social networks. Research in Learning Technology. 20:1-14.
Hommes et al. (2012). Visualising the invisible: a network approach to reveal the informal side of student learning. Advances in Health Sciences Education, 17(5), 743-757.
Kop, R. (2012). The Unexpected Connection: Serendipity and Human Mediation in Networked Learning. Educational Technology & Society, 15 (2), 2–11.
Martino, F. And Spoto, A. (2006). Social Network Analysis: A brief theoretical review and further perspectives in the study of Information Technology PsychNology Journal 4(1), 53 – 86.
Morrison, E. W. (2002). Newcomers’ relationships: the role of social network ties during socialization. Academy of Management Journal, 45(6), 1149–1160.
Scott J., Carrington P.C. (eds) (2011) Handbook of social network analysis. Sage, London
Yu, A.Y. et al. (2010). Can learning be virtually boosted? An investigation of online social networking impacts. Computers and Education, 55, pp. 1494–1503