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Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
Institutional future of Social Media in Higher Ed
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Institutional future of Social Media in Higher Ed

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An exploration of the impact of social media on higher education and the

An exploration of the impact of social media on higher education and the

Published in: Technology, Business
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  • 1. Institutional Future of Social Media Dr Ian Boston University of Cambridge
  • 2. Social Media ?
  • 3. Some rights reserved by Maschinenraum
  • 4. Institutional Context• We are people not buildings• Too late? • Club Penguin, Moshi Monsters, GenY
  • 5. Outsource• “Does Kmart ask MySpace to tell it what its customers buying patterns are?”• “Is LinkedIn the way to build great researcher teams?”
  • 6. Harvard Catalysthttp://catalyst.harvard.edu/home.html
  • 7. Institutional Memory
  • 8. Analysis and Actions
  • 9. Realistic Scale
  • 10. Data Gathering• Official Sources, SIS, HR, Finance• Collaboration trails• Starving for raw data
  • 11. Outsource by default• Nature abhors a vacuum• Order or Desire
  • 12. Its Our Domain• Surprisingly deep• Borrow, beg, buy• Head start
  • 13. The task ahead• Encourage Connections• Data gathering• Academic Apps
  • 14. Encourage Connections Profiles Collaboration Groups ColleaguesPublications Sharing
  • 15. Data Gathering Profiles Collaboration Groups Activity Data ColleaguesPublications Sharing
  • 16. Sakai 3 App Profiles Publications Sakai 3 App Sakai 3 App Groups Sakai 3 App Colleagues Sakai 3 App Sakai 3 App Sharing Collaboration Sakai 3 AppSakai Apps Framework Sakai 3 App Sakai 3 App Google Gadget Academic Apps Google Gadget Google Gadget Gadget Activity Data Container Google Gadget
  • 17. Improving Experience User Event Activity Records Enhanced Offline Data Analysis
  • 18. Important Maxims• Unbiased Observations.• Original data.• Analysis without expectations• Uncertainty.
  • 19. Example: Identifying Interests User authors Content content Created EventUser sees related Content created event, is pickedcontent, which authors up by a processor, analyses andare connected, which are categorises the content based onnot, offered to expand word used, generating links tonetwork of collaborators. similar content.
  • 20. Example: Identifying Behaviour Usage And Normal Expectation Student A Activity throughout the system Student has been identified ashaving erratic engagement withpeers and the course. Mentors, alerted that there may be an underlying cause.
  • 21. 5 years hence• TL&R tools built to support the needs of the members of all institutions.• Increasing availability of data sets within institutions to further the understanding of TL&R.• Less resource spent on the mundane, more on the unique. Thank you - Questions ?

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