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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. 

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.