For the past several years, EDUCAUSE publications have described the emergence of two complementary forces: (1) the growth of "academic analytics" in higher education and the knowledge services needed to support seamless sharing and leveraging of contextualized data/information; and (2) the escalating accountability demands that are driving performance measurement and improvement initiatives. These forces converged in the July/August 2007 issue of EDUCAUSE Review, which showcased their potentially transformative impacts on higher education

Last week ReadWriteWeb asked: "Is Linked Data Gaining Acceptance?" Our answer: definitely yes. Projects like DBPedia, a community effort to structure the information from Wikipedia and provide it as Linked Open Data, have come a long way and work really well. For example, you can search for all scientists born in Zürich, Switzerland.

Ah… the semantic web. The saviour of the internet, and the evil empire enforcing its evil standardization upon my freedom. I've always been a little suspicious of this particular topic. Not that I'm opposed to any kind of stardardization, railroads and the lack of standardizations with bank cards at grocery stores come to mind (grrr…) But the semantic web and how data is 'linked' is pretty important to analytics. time to dive in.

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."

After visualizing chat room dialogue earlier this week, I thought it might be neat to try and visualize some Twitter dialogue from the #LAK11 course.  For some reason I thought it would be simple to extract tweets from Twitter for analysis.  It wasn't!  So if you happen to know an easier way please leave a comment.

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.