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dc.contributor.authorSaward, Guy
dc.contributor.authorJefferies, Amanda
dc.contributor.editorNovotna, Jarmila
dc.contributor.editorJancarik, Antonin
dc.date.accessioned2017-05-02T16:08:59Z
dc.date.available2017-05-02T16:08:59Z
dc.date.issued2016-10-26
dc.identifier.citationSaward , G & Jefferies , A 2016 , Validating a social media typology with machine learning and focus groups . in J Novotna & A Jancarik (eds) , Proceedings of the 15th European Conference on E-Learning . ACPI (Academic Conference Publishing International) , Reading, UK , pp. 640-649 , ECEL 2016 , Prague , Czech Republic , 27/10/16 .
dc.identifier.citationconference
dc.identifier.isbn978-1-911218-18-0
dc.identifier.isbn978-1-911218-17-3
dc.identifier.otherPURE: 10607964
dc.identifier.otherPURE UUID: 92c7170b-8cff-4222-a96d-9b749917692a
dc.identifier.otherScopus: 85000399386
dc.identifier.otherORCID: /0000-0001-9545-1709/work/32509174
dc.identifier.urihttp://hdl.handle.net/2299/18134
dc.descriptionThis document is the Accepted Manuscript of the following paper: Guy Saward and Amanda Jefferies, ‘Validating a social media typology with machine learning and focus groups’, in Proceedings of the 15th European Conference on E-Learning. Prague, Czech Republic 27-28 October 2016. Jarmila Novotna and Antonin Jancarik eds., ISBN 978-1-911218-18-0, e-ISBN 978-1-911218-17-3. Published by Academic Conference Publishing International (ACPI).
dc.description.abstractSocial 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 everen
dc.format.extent10
dc.language.isoeng
dc.publisherACPI (Academic Conference Publishing International)
dc.relation.ispartofProceedings of the 15th European Conference on E-Learning
dc.rightsOpen
dc.subjectsocial media networks
dc.subjectstudent engagement
dc.subjectacademic engagement
dc.subjectComputer Science Applications
dc.subjectEducation
dc.subjectCultural Studies
dc.titleValidating a social media typology with machine learning and focus groupsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionWeight and Obesity Research Group
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2016-10-26
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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