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Social Media and Student Learning: Using Analytics to Visualise Twitter Communication in the Classroom
- 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
- 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)
- 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
- 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
- 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.
Editor's Notes
- #15: 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.
- #17: 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.
- #18: Bandwidth of the senses – convert that notion into computer terms – Tor Norretranders is a Danish scholar
- #31: 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.
- #32: Choose text to analyse – choose tool to run (find word patterns, view word use, trends in word use)