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These, and other similar projects, promise the emergence of a new social and theoretical paradigm whose goal is to decipher the web of social interactions generated by social media.

As the integration of community-centred teaching practices intensifies, an understanding of the types of relationships that manifest in this network and the associated impact on student learning is required. This paper explores the relationship between a student's position in a classroom social network and their reported level of sense of community. Quantitative methods, such as Rovai's (2002b) Classroom Community Scale and social network centrality measures, were incorporated to evaluate an individual's level of sense of community and their position within the classroom social network. Qualitative methods such as discussion forum content analysis and student interviews were adopted to clarify and further inform this relationship. The results demonstrate that the centrality measures of  closeness and  degrees are positive predictors of an individual's reported sense of community whereas,  betweenness indicates a negative correlation. Qualitative analyses indicate that an individual's pre-existing external social network influences the type of support and information exchanges an individual requires and therefore, the degree of sense of community ultimately experienced. The paper concludes by discussing future recommendations for teaching practices incorporating computer-mediated communications. 

From retailing to counterterrorism, the ability to analyse social connections is proving increasingly useful

What new things could we discover if social network analysis took time and space into account, in addition to the raw connections between people?

Goodhart's law states that once a social or economic indicator or other surrogate measure is made a target for the purpose of conducting social or economic policy, then it will lose the information content that would qualify it to play such a role.

Singapore's government has also provided substantial support for the Living Analytics Research Centre. The Centre, a research partnership between Carnegie Mellon and Singapore Management University, "seeks to make Singapore one of the world's premier locations for the development and applied use of real-time consumer and social analytics, as well as one of the world's leading centres for computational social science related R&D and education."

UCINET is a social network analysis program developed by Steve Borgatti, Martin Everett and Lin Freeman. The program is distributed by Analytic Technologies. UCINET works in tandem with freeware program called NETDRAW for visualizing networks. NETDRAW is installed automatically with UCINET.

Summify automatically identifies the most important news stories for you across all of your social networks and delivers to you a personalized email digest in your inbox.

With so many websites to join, users must decide where to invest significant time in adding their same connections over and over. For developers, this means it is difficult to build successful web applications that hinge upon a critical mass of users for content and interaction. With the Social Graph API, developers can now utilize public connections their users have already created in other web services. It makes information about public connections between people easily available and useful.

Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships. Management consultants use this methodology with their business clients and call it Organizational Network Analysis [ONA].

NetMiner is an innovative software tool for Exploratory Analysis and Visualization of Network Data. NetMiner allows you to explore your network data visually and interactively, and helps you to detect underlying patterns and structures of the network

Social and technological networks don't have a centre. When we learn in a classroom or in a learning management system (LMS), a central place exists where we can go for readings and

Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. EDUCAUSE's Next Generation learning initiative offers a slightly different definition "the use of data and models to predict student progress and performance, and the ability to act on that information". Their definition is cleaner than the one I offer, but, as I'll detail below, is intended to work within the existing educational system, rather than to modify it. I'm interested in how learning analytics can restructure the process of teaching, learning, and administration.

Today, we measure the size of the Web in exabytes and are uploading to it 15 times more data than we were 3 years ago. Technologies for sensing, storing, and sharing information are driving innovation in the tools available to help us understand our world in greater detail and accuracy than ever before. The implications of analyzing data on a massive scale transcend the tech industry, impacting the environmental sector, social justice issues, health and science research, and more. When coupled with astute technical insight, data is dynamic, accessible, and ultimately, creative. Marissa Mayer will speak to the power of data and the role it plays in Google's innovation. She will present on the technology trends that are changing our relationship with data, discuss fresh Google products that creatively put data to work, and offer her vision for the future of data in driving the Web forward.