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Social Networks: Analysing relationships in learning communities


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  • 1. Social NetworksAnalysing relationships in learning communities Andrew Deacon Centre for Educational Technology University of Cape Town EDN6099F – 20 March 2013
  • 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. 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. Predicting success MAT – School mathematics test (university admissions)Chemistry – 1st year university exam (first-year success)
  • 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. Predicting success
  • 7. Predicting success
  • 8. Predicting success
  • 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. 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. Beyond the institution context Social Media / PLEs / CoP
  • 12. UCT and social mediaProminent links to: – Facebook – Flickr – LinkedIn – Twitter
  • 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. Twitter: apps & locationsBlackberry Twitter Ubersocial Others 17% Blackberry 27% Smartphone geo-location 20% 36% Cell phones
  • 15. Twitter: tweeter relationshipsFrequent tweeters:1. Drama student (162)2. UCT Radio (132)3. Science student (84)
  • 16. Twitter: viral #UCT6 months of tweets Varsity Cup final Helicopter crash
  • 17. Flickr: helicopter crash at UCT Ian Barbour -
  • 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. Ingredient Networks for Recipe RecommendationsLada Adamic
  • 20. Facebook: all friend relationships Paul Butler
  • 21. LinkedIn Maps
  • 22. Within the institutional contexts Course data / LMS
  • 23. 1st-year coursecombinations HS HUMCOM SCI EBE
  • 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. 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:
  • 26. Students’ use of Vula in a course Submission of assignmentsPolling ofstudents Site visits Content accessed Chat room activitySectioningof students
  • 27. Sociogram of a discussion forum Dawson (2010)
  • 28. Words in chats used by failing students
  • 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. Effective visualisationsThe success of a visualization isbased on deep knowledge andcare about the substance, and thequality, relevance and integrity ofthe content. Tufte (1981)
  • 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. 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. 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. 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).• 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. Font references• FatFonts by Miguel Nacenta, Uta Hinrichs and Sheelagh Carpendale. The area of each number is proportional to its value Source: