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Towards identifying Collaborative Learning groups using Social Media
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Towards identifying Collaborative Learning groups using Social Media

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  • 1. Selver SofticSocial LearningTOWARDS IDENTIFYING COLLABORATIVELEARNING GROUPS USING SOCIAL MEDIA
  • 2. Agenda• Motivation• Problem statement• Methodology• Concept• Implementation• Evaluation• Conclusion and future work
  • 3. Motivation• Web 2.0• User generated content• Social Networks• Microblogging• Twitter http://blog.socialmaximizer.com/wp-content/uploads/2012/09/Social-Media.jpg
  • 4. Motivation• 57% of people talk to people more online than they do in real life• 40% of Twitter users don’t tweet, but instead use it to keep up to date• A great majority of tweets are just 40 characters long• Social media use is becoming much more even across age groups (see graph below) http://thesocialskinny.com/100-social-media-statistics-for-2012/
  • 5. Motivation ctd.• Huge amount of informations• Sharing of interests, experiences etc.• no cultural or georgraphical boundaries• Implicit knowledge• Appliances: conferences, course support, viral marketing
  • 6. Problem statement• Cluster users into sub-networks based upon their interest using topic items and social relations• Provide a filtered view on information generated in their micro sub-networks• Which methods or technologies would be suitable for this challenge?• Define and evaluate the metrics that can be used to achieve this goal!
  • 7. Methodology• Basic metrics – #hashtags – @mentions – occurrence• Evaluation tools: – Cosine Similarity, Euclidian Distance, Thresholds• Focus on relevant information carriers
  • 8. Concept: interest group G(i) tc,tl H α α δ α I(i)
  • 9. Implementation• Reference source – Grabeeter database – 1600 users – approx. 4,7 million tweets http://grabeeter.tugraz.at/• Reference data base – 100 users talking on term „e-learning“ – always last 250 hundred tweets considered• Verfication account• Scaling the input vectors• Thresholds: 10% and 20%
  • 10. Implementation
  • 11. Implementations ctd.• Similarity API – user to user – user to user group • user grou can be randomised
  • 12. Evaluation
  • 13. Evaluation
  • 14. Evaluation
  • 15. Evaluation
  • 16. Conclusion and future work• Results encouraging but: – More accurate and qualitative evaluation of clustering – Involving other methods Pearson, Jaccard – Extending the measurement on more appliance cases and reference users regarding the collaborative learning issues• Later: k-means, hierarchical clustering
  • 17. Contact Twitter: @selvers Mail: selver.softic@tugraz.at Slideshare: selvos Linkedin:http://at.linkedin.com/pub/selver-softic/24/33b/211

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