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The father of the World Wide Web, Tim Berners-Lee defines the Semantic Web as "a web of data that can be processed directly and indirectly by machines."

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. 

With Gapminder Desktop you can show animated statistics from your own laptop

Needlebase allows you to view web pages through a virtual browser, point and click to train it in understanding what fields on that page are of interest to you and how those fields relate to each other. Then the program goes and scrapes the data from all of those fields, publishes them into a table, list or map, and recommends merges of cells that appear to be mistakenly separate. It's very cool and it lets non-technical people do things with data quickly and easily that we used to require the assistance of someone more technical to do.

Academic analytics helps address the public's desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide. Purdue University's Signals project applies the principles of analytics widely used in business intelligence circles to the problem of improving student success within a course and, hence, improving the institution's retention and graduation rates over time.

And I'm saying the semantic web won't work. Can't work.

The British universities, Oxford and Cambridge included, are under siege from a system of state control that is undermining the one thing upon which their worldwide reputation depends: the caliber of their scholarship. The theories and practices that are driving this assault are mostly American in origin, conceived in American business schools and management consulting firms. They are frequently embedded in intensive management systems that make use of information technology (IT) marketed by corporations such as IBM, Oracle, and SAP. They are then sold to clients such as the UK government and its bureaucracies, including the universities. This alliance between the public and private sector has become a threat to academic freedom in the UK, and a warning to the American academy about how its own freedoms can be threatened.

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].

Demdex captures behavioral data on behalf of Websites and advertisers and stores it in a "behavioral data bank." The company then score each user across more than 40 behavioral and demographic variables to come up with a "traitweight," which helps websites segment their audiences better and advertisers target their messages more exactly.