Social Media and Student Learning: Using Analytics to Visualise Twitter Communication in the Classroom
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Social Media and Student Learning: Using Analytics to Visualise Twitter Communication in the Classroom

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  • A picture is worth 1,000 words refers to the idea that a complex idea can be conveyed with just a single still image. It also aptly characterizes one of the main goals of visualization, namely making it possible to absorb large amounts of data quickly.
  • Reference model for visualization. Visualization can be described as the mapping of data to visual form that supports human interaction in a workspace for visual sense making.
  • Bandwidth of the senses – convert that notion into computer terms – Tor Norretranders is a Danish scholar
  • You have two choices: use one of the  existing data sets    on the site, or Upload your own data set - After you choose a data set, you must choose a visualization method. Many Eyes provides a variety of visualization methods. Analyse text, see the relationships associated with part of the data, see relationships among different variables, track rises and falls in data over time.
  • Choose text to analyse – choose tool to run (find word patterns, view word use, trends in word use)

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  • 1. Social Media and Student Learning: Using Analytics to Visualise Twitter Communication in the Classroom Sharon Stoerger PELC11 April 7, 2011 [email_address]
  • 2. Agenda
    • Social media
      • What is it & why is it valuable?
      • Why Twitter?
    • Information visualisation
      • What is it?
      • Why should I visualise?
      • What are educational uses of information visualisation?
    • Visualising Twitter data
    • The future
  • 3. What is social media?
  • 4. One Definition (boyd & Ellison, 2007)
    • Web-based services that allow individuals to:
      • construct a public or semi-public profile within a bounded system;
      • articulate a list of other users with whom they share a connection ; and
      • view and traverse their list of connections and those made by others within the system. 
    • http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html
  • 5. Why is social media valuable?
    • Increase communication
    • Increase feelings of connectivity
    • Increase online learning community
    • Increase learning
  • 6. http://twitter.com
  • 7.  
  • 8. Why Twitter?
    • Microblogging (140 characters)
    • Easy-to-use
    • Push down communication
    • Not email
      • Zero clutter
      • Students  social media > email (Roblyer et al., 2010)
    • Personal Learning Network (PLN)
      • Learning through connections
      • Connectivism (Siemens, 2004)
  • 9. Definitions (Card et al., 1999, p. 7)
    • Visualisation:
    • The use of computer-based, interactive visual representations of data to amplify cognition.
    • Information visualisation:
      • The use of interactive visual representations of abstract, nonphysically based data to amplify cognition.
  • 10. What is information visualisation?
    • Robertson, Card, & Mackinlay (1989)
      • First use of the term “information visualisation”
      • Cognitive amplification, interactivity, animation
    • Represent data – visual form
    • External cognition aids
      • Maps, charts, graphs, diagrams
      • Text clouds, animations
      • Social media relationships (e.g., Hansen, 2011)
      • Mashups (e.g., Google Maps/Google Earth)
  • 11. “ Evolution” of Information Visualisation
  • 12. Information Visualisation = Mainstream
    • Today’s tools
      • Free, interactive
      • Bring data to non-experts
    • Journalists
      • NY Times
      • http://tinyurl.com/45md7ur
    • Artists
      • Brooke Singer
      • Databody
      • http://www.bsing.net/databody.pdf
  • 13. Q: WHY SHOULD I VISUALISE?
  • 14. A Picture is Worth 1,000 Words Pictures can attract attention faster than other media (Barnard, 1927)
  • 15.  
  • 16. Reference Model for Visualisation (Card et al., 1999, p. 17)
  • 17. The Language of the Eye
    • The User Illusion (1999)
    • Sight  faster
      • Bandwidth
      • Computer network
    • Better understanding
      • Eye
      • Mind
  • 18.
    • http://www.octium.eu/en/index.php/information-systems
  • 19. TMI: Too Much Information
    • Twitter users (e.g., Rao, 2011)
      • 572,000 accounts  created on March 12, 2011
      • 460,000 (ave.) new accounts/day
      • Mobile users are up 182% from 2010
    • Tweets – the numbers
      • 140 million Tweets (ave.)/day
      • 50 million Tweets sent per day, a year ago 
      • Record tweets = 177 million  March 11, 2011
  • 20. Visualising Twitter Traffic http://vimeo.com/11302556
  • 21. Visualisation = Data Compression
    • David McCandless, 2010
    • Data is the new oil
    • Or is data the new soil?
      • Fertile
      • Well-tilled medium
      • Visualisations = data flowers
  • 22. Education-related Reasons to Visualise
    • Insight (not pictures)
      • New way to see & experience information
      • Hidden patterns, connections = revealed
      • Narrative = clarified
    • Amplify cognition - sense making (Card et al., 1999; Larkin & Simon, 1987)
    • Self-organising maps = brain organisation
    • Integrate offline-online experiences
    • Digital & critical competencies
    Image: http://www.brainandlearning.eu/
  • 23. Information Visualisation Example
    • Ward Shelley’s “History of Science Fiction”
    • Rhetorical drawings
    • http://scimaps.org/submissions/7-digital_libraries/maps/thumbs/024_LG.jpg
  • 24. Visualisation Activities
    • Reimagine existing assignments
    • “ Software Studies” (Manovich, 2008)
      • Use & evaluate software
      • Limitations & biases
      • Influence
    • Analyse and produce visualisations
      • Visual literacy
      • Functional literacy (Selber, 2004)
  • 25. What Twitter information can I visualise?
    • Twitter
    • Tweets (e.g., @csoleil)
    • Hashtags (e.g., #socmedia)/backchannel communication
    • Retweets
    • Replies
    • Links
    • Projects
    • Text
    • Personal data
    • Social data
    • Create = digital artifacts
  • 26. HOW DO I VISUALIZE TWITTER DATA?
  • 27. Text clouds: Wordle http://www.wordle.net/
    • Common text visualiser
    • “ A toy for generating word clouds”
  • 28. Text Cloud: Tagxedo http://www.tagxedo.com/
  • 29. Text & Hashtag Clouds: TweetStats http://tweetstats.com/
  • 30. Wordle Plus: Many Eyes http://www-958.ibm.com/software/data/cognos/manyeyes/
    • “… like Facebook for infovis nerds” (Sorapure, 2009, p. 63)
    • IBM researchers (Fernanda Viegas, Martin Wattenberget, etc.)
  • 31. Text Analysis Portal for Research (TAPoR) http://portal.tapor.ca/portal/portal
    • Tools  analysis and retrieval
    • Representative texts  experimentation
  • 32. Conversations: Twitterfall http://twitterfall.com/
    • Real time tweet searching
    • New tweets fall on the page
    Pause tweets
  • 33. Statistics: TweetStat http://tweetstats.com/
  • 34. Networks: Mentionmap http://apps.asterisq.com/mentionmap/#
  • 35. Twitter Friends Network Browser http://www.neuroproductions.be/twitter_friends_network_browser/
  • 36. Visualisation Concerns
    • “ Eye candy”
      • “ Chart junk” graphics (Card et al., 1999)
      • Graphical distortion - highlights anomalies (Tufte, 1983)
    • Ease-of-use
      • Less familiar with data sets
      • Not fully understand data
      • Mislead/confuse consumers
    • Evaluation of effectiveness
      • Criteria, measurements, methods???
      • Experience subjectivity
  • 37. Rashômon (4 versions of the truth)
    • http://www.youtube.com/watch?v=xCZ9TguVOIA
  • 38. What’s Next?
    • Programs
      • National Visual Analytics Centers (NVACs) - 2005
      • Analyse agency information needs
    • Disciplines
      • Technology, art, science (van Wijik, 2005)
      • Humanities
      • Education
    • Tools
      • Dashboards, visual analytics, simple graphs
      • Interactive visualisations
      • Mobile applications  Public participation
  • 39. The Future? http://www.rottentomatoes.com/m/minority_report/trailers/11129681
  • 40. Thank You!!!
    • Questions?
    • Sharon Stoerger
    • Email: [email_address]
    • Facebook: sharon.stoerger
    • Twitter: csoleil
    • Second Life: Cerulean Soleil
  • 41. Read More About It
    • Card, S. K., Mackinlay, J. D., Shneiderman, B. (1999). Readings in information visualization . San Francisco, CA: Morgan Kaufmann Publishers, Inc.
    • Few, S. (2010). Information visualization, design and the arts: Collision or collaboration? Visual Business Intelligence Newsletter.
    • Johnson, L., Levine, A., Smith, R., Stone, S. (2010). The 2010 horizon report. Austin, TX: The New Media Consortium.
    • Larkin, J., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11 (1), 65-99.
    • Manovich, L. (2010). What is visualization. http://manovich.net/2010/10/25/new-article-what-is-visualization/
    • Moretti, F. (2005). Graphs, maps, trees: Abstract models for a literary history . London: Verso
    • Sorapure, M. (2009). Information visualization, Web 2.0, and the teaching of writing. Computers and Composition, 27, 59-70.
    • Tufte, E. R. (1983). The visual display of quantitative information . Cheshire, CT: Graphic Press.
    • van Wijk, J. J. (2005). The value of visualization. In C. Silva, E. Groeller, H. Rushmeier (eds .), Proceedings of IEEE Visualization 2005 , 79-86. 
    • Ware, C. (2004). Information visualization: Perception for design, 2nd ed. San Francisco: Morgan Kaufmann Publishers, Inc.