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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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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 Moreni 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.

Nodes can be anything not just a person. They could be an email, discussion post…

The degree of a node calculated by the number of edges that are adjacent to it. So by ranking each node within a social network by degree, we can distinguish which individuals have the most connections (Figure 6). Source http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is-believing/

Betweenness Centrality measures how often a node appears on the shortest paths between nodes in a network. So by ranking each node within a social network by betweenness centrality, we can distinguish which influential individuals have the most connections across distinct community clusters (Figure 7). Source http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is-believing/

The PageRank graph is generated by having all of the World Wide Web pages as nodes and any hyperlinks on the pages as edges. The edges are further characterized as weak or strong edges by weighting the edges. Pages that are linked by more credible sources such as CNN or USA.gov sites have higher weightings for the respective edges. Thus, if we compare two sites with the same number of edges. PageRank will give the site with more links to credible sources a better rank. Source: http://blogs.cornell.edu/info2040/2011/09/20/pagerank-backbone-of-google/

This type of network graph is often called a hairball … or as I call it the big ball of timey, whimy, whibbly wobbly stuff.

- 1. Centre for Research in Amplified Practice
- 2. Image: CC-BY-NC-ND the_forgotten_nomad https://flic.kr/p/kubCDA
- 3. Making the complex less complicated: An introduction to network analysis Martin Hawksey @mhawksey #iltaedtech17 http://go.alt.ac.uk/iltaedtech17-networks This work is licensed under a Creative Commons Attribution 4.0. CC-BY mhawksey
- 4. Image: CC-BY m.hawksey https://flic.kr/p/qbMRze © RAND Corporation 1964 On Distributed Communications: 1. Introduction to Distributed Communications Network
- 5. Volume of data pre 2015 Volume of data since 2015
- 6. CC-BY-NC katie wheeler https://flic.kr/p/jmiuEG
- 7. alt.ac.uk Moreno (1934) Who Shall Survive? Copyright: Nervous and Mental Disease Publishing Co. Origins @mhawksey
- 8. alt.ac.uk Node or vertex Node or vertex Edge or link Edge or link Basics @mhawksey
- 9. alt.ac.uk Ingram (2012), Visualising Data: Seeing is Believing http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is-believing/ Network Measures @mhawksey
- 10. alt.ac.uk Ingram (2012), Visualising Data: Seeing is Believing http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is-believing/ Network Measures @mhawksey
- 11. alt.ac.uk PageRank Image Public Domain https://commons.wikimedia.org/wiki/File:PageRanks-Example.svg @mhawksey
- 12. alt.ac.uk Making networks @mhawksey
- 13. alt.ac.uk Examples Bakharia and Dawson (2011) SNAPP: A Bird’s-eye View of Temporal Participant Interaction https://www.slideshare.net/aneeshabakharia/snapp-learning-analytics-and-knowledge-conference-2011 Learner Isolation Facilitator Centric @mhawksey
- 14. alt.ac.uk Examples Bakharia and Dawson (2011) SNAPP: A Bird’s-eye View of Temporal Participant Interaction https://www.slideshare.net/aneeshabakharia/snapp-learning-analytics-and-knowledge-conference-2011 Non Interacting Groups Facilitator Bias @mhawksey
- 15. CC-BY Magnus Bråth https://flic.kr/p/9doZ1j Fur Ball
- 16. alt.ac.uk Examples Situational Awareness @mhawksey #ukoer hashtag community 2010 CC-BY psychemedia https://flic.kr/p/8JBzAo
- 17. “ alt.ac.uk 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. alt.ac.uk CC-BY-SA miss Murasaki https://flic.kr/p/bCafgG Paws
- 19. alt.ac.ukEric Berlow: Simplifying complexity https://www.ted.com/talks/eric_berlow_how_complexity_leads_to_simplicity @mhawksey
- 20. alt.ac.ukEric Berlow: Simplifying complexity https://www.ted.com/talks/eric_berlow_how_complexity_leads_to_simplicity @mhawksey
- 21. alt.ac.ukEric Berlow: Simplifying complexity https://www.ted.com/talks/eric_berlow_how_complexity_leads_to_simplicity @mhawksey
- 22. T A G S . H A W K S E Y . I N F O go.alt.ac.uk/iltaedtech17-tags
- 23. alt.ac.uk Tools for exploratory analytics @mhawksey
- 24. alt.ac.uk 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. alt.ac.uk 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. alt.ac.uk Thank you! @mhawksey+MartinHawksey http://go.alt.ac.uk/iltaedtech17-networks @mhawksey
- 27. Association for Learning Technology Registered charity number: 11600399 www.alt.ac.uk @A_L_T

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