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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. 

Data analytics is shorthand for the method of warehousing, organizing, and interpreting the massive amounts of data accrued by online learning platforms and student information systems - now as elemental to higher education as classrooms and filing cabinets - in hopes of learning more about what makes students successful, then giving instructors (and the platforms themselves) the chance to nudge those students accordingly.

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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

Here we've got a classmate PC demo that shows off Richard Beckwith shows off Classmate Assist which supports users seated or standing at the desk or table. This is an example of context aware computing. There are visual sensors that recognize the items on the table and instruct and walk the students through a series of educational tasks. The goal is to support existing curriculum and practices of teachers.