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Making the complex less complicated: An introduction to social network analysis


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Presented at ILTA EdTech 2017, Sligo, Ireland

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Patterns are left behind. Whether it be replies to a discussion forums, interactions on social media or ingredients in cocktails links can be made and the data used for actionable insight. Network science is one approach that takes these seemingly complex connections and through the use of mathematical methods make it easier to understand. Network science is a well established discipline and it’s origins can be traced to 1736 and the work of Leonhard Euler. The area of social network analysis is a more recent development established in work by Moreno and Jennings in the 1930s. Accessibility to affordable computing in the 1990s combined with data from early social networks like IRC has led to an explosion of interest in social network analysis. This has continued with the emergence of social networking sites like Facebook and Twitter combined with accessibility to the underlying data. The use of network science and social network analysis within educational contexts has seen similar growth. The emergence of ‘Learning Analytics’ as a field of study has highlighted how data can be used to enhance learning and teaching. With social network analysis we can take seemingly complex relationships and making them less complicated. Common applications of network analysis in this area include: identification of isolated students within group activities; identification of people or concepts which are ‘network bridges’; clustering of categorisation of topics; plus numerous other applications.

This presentation is designed to be an introduction into network analysis allowing delegates the opportunity to understand the underlying structure of the graph as well as some of the tools that can be used to construct them. The session will begin with an introduction to key network analysis terms and go on to introduce some of the tools and techniques for social network analysis, specifically looking at how data can be collected and analysed from Twitter using tools like TAGS and NodeXL.

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Making the complex less complicated: An introduction to social network analysis

  1. 1. Centre for Research in Amplified Practice 
  2. 2. Image: CC-BY-NC-ND the_forgotten_nomad
  3. 3. Making the complex less complicated: An introduction to network analysis Martin Hawksey @mhawksey #iltaedtech17 This work is licensed under a Creative Commons Attribution 4.0. CC-BY mhawksey
  4. 4. Image: CC-BY m.hawksey © RAND Corporation 1964 On Distributed Communications: 1. Introduction to Distributed Communications Network
  5. 5. Volume of data pre 2015 Volume of data since 2015
  6. 6. CC-BY-NC katie wheeler
  7. 7. Moreno (1934) Who Shall Survive? Copyright: Nervous and Mental Disease Publishing Co. Origins @mhawksey
  8. 8. Node or vertex Node or vertex Edge or link Edge or link Basics @mhawksey
  9. 9. Ingram (2012), Visualising Data: Seeing is Believing Network Measures @mhawksey
  10. 10. Ingram (2012), Visualising Data: Seeing is Believing Network Measures @mhawksey
  11. 11. PageRank Image Public Domain @mhawksey
  12. 12. Making networks @mhawksey
  13. 13. Examples Bakharia and Dawson (2011) SNAPP: A Bird’s-eye View of Temporal Participant Interaction Learner Isolation Facilitator Centric @mhawksey
  14. 14. Examples Bakharia and Dawson (2011) SNAPP: A Bird’s-eye View of Temporal Participant Interaction Non Interacting Groups Facilitator Bias @mhawksey
  15. 15. CC-BY Magnus Bråth Fur Ball
  16. 16. Examples Situational Awareness @mhawksey #ukoer hashtag community 2010 CC-BY psychemedia
  17. 17. “ Graphs can be a powerful way to represent relationships between data, but they are also a very abstract concept, which means that they run the danger of meaning something only to the creator of the graph. Often, simply showing the structure of the data says very little about what it actually means, even though it’s a perfectly accurate means of representing the data. Everything looks like a graph, but almost nothing should ever be drawn as one. Ben Fry in ‘Visualizing Data’ @mhawksey
  18. 18. CC-BY-SA miss Murasaki Paws
  19. 19. Berlow: Simplifying complexity @mhawksey
  20. 20. Berlow: Simplifying complexity @mhawksey
  21. 21. Berlow: Simplifying complexity @mhawksey
  22. 22. T A G S . H A W K S E Y . I N F O
  23. 23. Tools for exploratory analytics @mhawksey
  24. 24. Key points ◊ Getting to this and you are over 80% or the way ◊ There are a lot of very knowledgeable people in the community willing to help ◊ Go explore … and have fun @mhawksey
  25. 25. Getting Social Network Data ◊ Using Twitter as a data source: an overview of social media research tools (updated for 2017) ◊ Twitter: How to archive event hashtags and create an interactive visualization of the conversation @mhawksey
  26. 26. Thank you! @mhawksey+MartinHawksey @mhawksey
  27. 27. Association for Learning Technology Registered charity number: 11600399 @A_L_T