The use of Facebook, in higher education, has become common place presumably due to a general belief that the platform can promote information flows between students and with staff as well as increasing a sense of community engagement. This study sets out to examine the academic use of Facebook groups using data analysis in order to determine if there are educational benefits and if Facebook group based learning strategies can be evaluated quickly and relatively easily. The data analysis involved utilising Social Network Analysis (SNA) in examining two Facebook groups; one under-graduate ‘course’ based group with 135 members and one under-graduate first year ‘module’ based group with 123 members. The SNA metrics included degree centrality, betweeness centrality, clustering coefficient and eigenvector centrality. The study also involved conducting a survey and interviews drawn from users of the Facebook groups to validate the utility of the SNA metrics. Results from the validation phase of the data analysis suggested that degree centrality is a useful guide to positive attitudes towards information flows, whilst betweenness centrality is useful for detecting a sense of academic community. The validation outcomes also suggest that high clustering coefficient scores were associated with a lower perception of academic community. The analysis of the data sets also found that the ‘course’ based group had higher scores for degree centrality and betweenness. This suggests that the ‘course’ based group provided a better experience of information access and a sense of academic community. Follow up interviews with respondents suggested that the ‘course’ based Facebook group may have had higher scores because it included more real world acquaintances than the ‘module’ based group.
|Number of pages||10|
|Journal||International Journal of Information Technology and Computer Science|
|Early online date||8 May 2019|
|Publication status||Published - May 2019|
- data analysis
- social network analysis
- social media