Sakai Social Network Visualisation
We have developed a visualisation tool for the exploration and analysis of a student social networks in the Sakai e-learning environment, or for more genral use as a standalone tool. The tool integrates techniques from social networking and graph theory and utilises the concepts of centrality and distance to implement a number of filtering and clustering functions. These functions help to reveal the embedded patterns in the underlying data and develop a more meaningful understanding of the interactions between students. Using this tool, an instructor can explore the student social network within a course and determine network attributes such as the cohesiveness and participation among students.
The visualisation tool is closely integrated with the Sakai Message Center tool and was written using the Java programming language and the Prefuse visualisation toolkit. The design of the visualisations are based on the simple edge-node graph model. In order to visualise the social network in an informative way, spatial position, color, size, thickness, and shape have all been used to visually encode information in the graph. The user interface provides many innovative techniques for exploring node connectivity. These include interactive techniques, such as dragging, zooming and panning, supporting filters and keyword searching.
The visualisations are built from the collection of collaborative user data that is generated by students interacting through the Sakai Message Center. The Enron email dataset has been used to populate the Sakai Message Center with real messages for testing.