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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)
  • Transcript

    • 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.

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