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

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