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Social Network Technologies for Learning


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Social network technologies are reforming the way we communicate with each other inside and outside our learning environments. In this presentation, Stephen Downes offers an inside look at these technologies, how they work, what they can do, and where they will likely lead the future of learning online. Downes will first outline some well-known technologies such as YouTube, Facebook and Twitter, describing how they are used and outlining how they manage online communication in general. He will describe and explain the design principles underlying these sites, and show how underlying web technologies such as representational state transfer and asynchronous Javascript are enabling the development of interactive online applications. He will show examples of how these services work together to provide interactive experiences and data management, and demonstrate how academic institutions are beginning to integrate their online services with social network technology. Based on these demonstrations, Downes will outline the potential of social network applications in the support of learning, and suggest concrete steps teachers and institutions can take now to improve learning using social networks. See also

Published in: Education, Technology, Business

Social Network Technologies for Learning

  1. 1. Overview: • The Major Social Networks • Anatomy of a Social Network • Social Networks in Learning • Platforms and Programs • Learning Network Applications
  2. 2. 1. The Major Social Networks
  3. 3. The Progenitors• Friendster –• MySpace –• Orkut –
  4. 4. Facebook
  5. 5. Twitter
  6. 6. Google+
  7. 7. Some of the Others:• Flickr –• LinkedIn –• YouTube –
  8. 8. 2. Anatomy of a Social Network
  9. 9. Personal Profile
  10. 10. The Graph• Facebook ‘friending’ vs Twitter ‘following’ vs Google+ ‘circling’• Recommending – friend suggestions or maybe just ‘return the favour’
  11. 11. Actions• Things to Do… – The Facebook ‘poke’ – Messaging – Checking In - – Listening to -• The activity stream
  12. 12. Likes and Dislikes• The ‘Like’ button / ‘Fan’ – Inside the Graph – Facebook – Outside the Graph – Google’s +1 – Extended graph – would you ‘recommend’ this person, write them an endorsement?• Tagging – – Twitter hashtags – word clouds
  13. 13. Groups• Explicit group formation: – Google Groups, Yahoo Groups• Object-based group formation – Facebook ‘causes’, ‘like’, etc. – Events
  14. 14. Sharing• Media – photo, video and audio uploads• Artwork – DeviantArt -• Experiences – the Twitter hashtag again• Places – 4Squarefake batman by *berkozturk, DeviantArt
  15. 15. Reputation• Klout –• HootSuite -• Social media key elements: PageRank, eBay Reputation, Amazon Reviews• Achievement: ‘become mayor’, ‘unlock badge’
  16. 16. 3. Social Networks in Learning
  17. 17. Some Benefits• It’s free• It cuts down on isolation• It builds tolerance and understanding of diversity• It amplifies passions• It’s more open, and education needs to be more openGeorge Couros, Connected Principals, Why Social Media Can and IsChanging Education
  18. 18. Positives• Connection and access• Increased awareness• Publicity and advertising• It’s who you knowWikia, Social Networking in Education Wiki
  19. 19. Risks• Privacy and security – putting your data out there – once online, always online – “you are not a customer, you are a product” Douglas Rushkoff, You Are Not Facebooks Customer
  20. 20. Rumours and Innuendo• Cyberbullying• Quality control – Twitter rumours
  21. 21. The Five intangibles• Attention• Attraction• Affinity• Audience• ActionThe Five Elements of Social MediaJay Deragon & David Bullock with Irene Williams Elements of Social Media.pdf
  22. 22. Social PresenceTwitter – “Whatare you Doing?”Anderson, Archer, GarrisonCommunity of Inquiry Model
  23. 23. Crowdsourcing• Wikipedia and such• The Wisdom of Crowds
  24. 24. Mapping SNs and Learning TheoryCentre for Research on Networked Learning and Knowledge Building,Development f Learning Theories
  25. 25. Network Effects• “A virtual learning environment consisting solely of students and instructor, in contrast, cannot partake of these network effects.” Bryan Alexander, EDUCAUSE, Social Networking in Higher Education
  26. 26. Social Network Policies• Schools banning Facebook?• “Discussions of privacy and copyright, and questions about creativity and appropriation, citizenship, and governance can become grounded in years of social media experience.” – Alexander Digizen, Case Study: Example Acceptable Use Policy px
  27. 27. Some SN Education Platforms• Ning –• Elgg -… and communities: – Classroom 2.0 - – -Association for Social Media in Higher Education -
  28. 28. 4. Platforms and Programs
  29. 29. To Understand Platforms…• Think of Mobile computing: – iPod, iPhone, iPad – Android, Windows Mobile, Blackberry• Next, think of ‘the App store’ – eg., the Apple app store• Apply the concept to a social network – inside the social network is an ‘app store’ – these apps access SN data and functionality
  30. 30. Some Analogies• Extensions in Firefox or Thunderbird• Modules in Drupal or WordPress• Google Apps in Chrome
  31. 31. ConnectingOAuth 2.0
  32. 32. OAuth PermissionsSachin Khosla ,,Upgrading Basic Twitter Authentication to OAuth with PHP
  33. 33. Identity and Login • Facebook Connect, Google SignOn, Twitter, OpenID • Disqus CommentsMark Aaron Murnahan, Why Disqus May Be The Best Social Network of 2011
  34. 34. RSS and AtomStephen Downes, An Introduction to RSS for Educational Designers
  35. 35. 5. Learning Network Applications
  36. 36. Distributed SN Architectures• Napster, Gnutella, BitTorrent• OpenID Again• Diaspora - – Distributed Social Networking Protocol -
  37. 37. The Semantic Social Network• Recommender systems• Metadata and metatags• Types of indices: – subject index – keyword index – citations and link indicesStephen Downes, Semantic Networks and Social Networks, PLEs for Contemplation and Awareness: An Architectural Approach
  38. 38. The Personal Learning EnvironmentMohamed Amine Chatti, Model-Driven Mashup Personal Learning Environments
  39. 39. Network Functionality• Pattern Recognition – eg. from partial data – eg. face recognition – Regression (prediction)• Similarity Functions – network-based recommendation• Clustering• Self-Organizing Feature Map (SOFP)Statsoft, Neural Networks
  40. 40. Emergent Properties• The whole is not the sum of the parts• Jarche: “Culture is an emergent property of people working together. Leadership is also an emergent property.”Harold Jarche, Leadership emerges from network culture
  41. 41. Groups and Networks
  42. 42. Some other ReferencesThomas Vander WalPersonal InfoCloud, The Elements in the Social Software Stack Bryant, TheNextWeb, Anatomy of a Twitter rumor: When a story’s too juicynot to retweet
  43. 43.