Validating a social media typology with machine learning and focus groups

Abstract

Social media networks (SMN) are an established part of the learning landscape in which our students reside as digital inhabitants. Our work is built around an ongoing four-year survey of student attitudes and engagement with SMN and their educational use. Our pre-conceptions were that students would be less keen on engaging with staff via social media. However, the survey results showed only 14% of students against this. Using machine learning to investigate whether those for academic SMN use (dubbed “integrationists”) could be separated from those against (“separatists”) showed it was hard to predict students’ attitudes purely based on their patterns of use of SMN. The complexity of the issues is reflected by focus group work that identified SMN as just one part of a complex pattern of personal communication. For some, Facebook (FB) consumed more time compared to text/email, but the latter were seen as more privileged with use restricted to higher value conversations and participants. Other insights included conflicted views on the value of SMN, a functional view of SMN alerts, and the lack of immersion in academic SMNs. These results suggest SMN are not a panacea for student engagement. Care must be taken in designing effective learning conversations using appropriate media and interaction. Slavishly adopting social practices from SMN will not automatically benefit learners and may leave them more disengaged and distracted than ever