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Social Networks: Analysing relationships in learning communities

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Social Networks: Analysing relationships in learning communities

  1. 1. Social NetworksAnalysing relationships in learning communities Andrew Deacon Centre for Educational Technology University of Cape Town EDN6099F – 20 March 2013
  2. 2. Outline• Measures of success• Looking at social networks• Data landscape in learning organizations• Interpreting relationships in social networks• Identifying trends in learning environments• Imagining future scenarios
  3. 3. Three eras of social research1. Age of Quételet – collect data on simple & important questions2. Classical period – inference theory to get the most information from a little data3. Present day big data – deluge of data and questions
  4. 4. Predicting success MAT – School mathematics test (university admissions)Chemistry – 1st year university exam (first-year success)
  5. 5. Predicting success Top quarterStudent MAT of both ChemistryStudent 1 66 42Student 2 90 92Student 3 74 51Student 4 63 58Student 5 73 69Student 6 73 68Student 7 88 90Student 8 81 77Student 9 69 61Student 10 64 66Student 11 81 75Student 12 92 88
  6. 6. Predicting success
  7. 7. Predicting success
  8. 8. Predicting success
  9. 9. If our aim is to understand people’sbehaviour rather than simply to recordit, we want to know about primarygroups, neighbourhoods, organizations,social circles, and communities; aboutinteraction, communication, roleexpectations, and social control.Allen Barton, 1968, cited in Freeman (2004) Source: CC BY-SA 3.0
  10. 10. Educational data landscape Institutional Individual (in wider Communities of Practice) Institutional data Personal Learning Social media & learning environments Environments (PLE) & social learning• ERP Systems• Historical performance data• Learning management system data• Libraries• School application data• Turnitin Reports• Demographics Data is Data is Data is • Accessible • Almost unattainable • Restricted • Can identify individuals • Difficult to link to individuals • Difficult to link to individuals
  11. 11. Beyond the institution context Social Media / PLEs / CoP
  12. 12. UCT and social mediaProminent links to: – Facebook – Flickr – LinkedIn – Twitter
  13. 13. Twitter: UCT chatter• Looked at 6 months of data April – Sept 2011• Selected tweets with a UCT hashtag or text #UCT, #Ikeys, University of Cape Town, …• Attributes tweet amplification, app used, location• Dataset Just over 5,000 tweets
  14. 14. Twitter: apps & locationsBlackberry Twitter Ubersocial Others 17% Blackberry 27% Smartphone geo-location 20% 36% Cell phones
  15. 15. Twitter: tweeter relationshipsFrequent tweeters:1. Drama student (162)2. UCT Radio (132)3. Science student (84)
  16. 16. Twitter: viral #UCT6 months of tweets Varsity Cup final Helicopter crash
  17. 17. Flickr: helicopter crash at UCT Ian Barbour - http://www.flickr.com/people/barbourians/
  18. 18. Twitter: helicopter crash at UCT• Peak of 140 tweets in 5 minutes• Media organisations tweets get re-tweeted• Crash or hard-landing? 2 hours after the event
  19. 19. Ingredient Networks for Recipe RecommendationsLada Adamic http://www.foodandtechconnect.com/siteold/2011/11/28/mining-allrecipes-coms-ingredient-networks-for-recipe-recommendations/
  20. 20. Facebook: all friend relationships Paul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919
  21. 21. LinkedIn Mapshttp://inmaps.linkedinlabs.com
  22. 22. Within the institutional contexts Course data / LMS
  23. 23. 1st-year coursecombinations HS HUMCOM SCI EBE
  24. 24. Maths and Maths Literacy UCT Humanities students course combinations 26% to 50% Maths LitMore than 50% Maths Lit No Maths Lit 1% to 25% Maths Lit
  25. 25. Purdue Universitys Course Signals• Early warning signs provides intervention to students who may not be performing well• Marks from course• Time on tasks• Past performance Source: http://www.itap.purdue.edu/learning/tools/signals
  26. 26. Students’ use of Vula in a course Submission of assignmentsPolling ofstudents Site visits Content accessed Chat room activitySectioningof students
  27. 27. Sociogram of a discussion forum Dawson (2010)
  28. 28. Words in chats used by failing students
  29. 29. Words used by Lecturers vs Students Marks; thanks;‘Weiten’ – test; textbook Tut; author guys Week; pages Used more by Used more byLecturers/tutors Students
  30. 30. Effective visualisationsThe success of a visualization isbased on deep knowledge andcare about the substance, and thequality, relevance and integrity ofthe content. Tufte (1981)
  31. 31. Correlation and causation• Correlation does not imply causation – Covariation is a necessary but not a sufficient condition for causality – Correlation is not causation (but could be a hint)
  32. 32. Future scenarios• Social networks in educational research: – Understanding social media & PLEs for learning – Institutional data from a student perspective – Connectionist theories of learning – Ethical considerations• Visualisations of social networks: – Good open source software available – Observation and analysis many outcome variables
  33. 33. Software references• Gephi – network analysis, data collection• NodeXL – network analysis, data collection• TAGS – Twitter data collection (Google Drive)• Word cloud – R package (wordcloud)• Geo-location map – R package (RgoogleMaps)• Excel – spreadsheet, charts• SPSS – statistical analysis, graphs
  34. 34. Literature references• Dawson, S. (2010) ‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736-752.• Deacon, A., Paskeviciusat, M. (2011) Visualising activity in learning networks using open data and educational analytics. Southern African Association for Institutional Research Forum, Cape Town.• Freeman, C. (2004) The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press: Vancouver, BC Canada.• Fritz, J. (2011) Learning Analytics. Presentation prepared for Learning and Knowledge Analytics course 2011 (LAK11). http://www.slideshare.net/BCcampus/learning-analytics-fritz• Hansen, D., Shneiderman, B., Smith, M.A. (2011) Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Morgan Kaufmann Publishers, San Francisco, CA.• Tufte, E. (1981) The visual display of quantitative information. Cheshire, Conn.: Graphics Press.
  35. 35. Font references• FatFonts by Miguel Nacenta, Uta Hinrichs and Sheelagh Carpendale. The area of each number is proportional to its value http://fatfonts.org Source: http://fatfonts.org

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