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Social Networks: Analysing relationships in learning communities
This document discusses analyzing social networks in learning communities. It outlines the data landscape in educational institutions, including institutional data, personal learning environments, and social media. It provides examples of visualizing and interpreting relationships in social networks using tools like Twitter, Facebook, LinkedIn, and course data. Finally, it discusses future scenarios for using social networks in educational research and visualization.
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Social Networks: Analysing relationships in learning communities
- 1. Social Networks Analysing relationshipsin 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 ofsocial research 1. Age of Quételet – collect data on simple & important questions 2. Classical period – inference theory to get the most information from a little data 3. 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 quarter Student MAT of both Chemistry Student 1 66 42 Student 2 90 92 Student 3 74 51 Student 4 63 58 Student 5 73 69 Student 6 73 68 Student 7 88 90 Student 8 81 77 Student 9 69 61 Student 10 64 66 Student 11 81 75 Student 12 92 88
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- 9. If our aimis to understand people’s behaviour rather than simply to record it, we want to know about primary groups, neighbourhoods, organizations, social circles, and communities; about interaction, communication, role expectations, 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 institutioncontext Social Media / PLEs / CoP
- 12. UCT and socialmedia Prominent 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 &locations Blackberry Twitter Ubersocial Others 17% Blackberry 27% Smartphone geo-location 20% 36% Cell phones
- 15. Twitter: tweeter relationships Frequenttweeters: 1. Drama student (162) 2. UCT Radio (132) 3. Science student (84)
- 16. Twitter: viral #UCT 6months of tweets Varsity Cup final Helicopter crash
- 17. Flickr: helicopter crashat UCT Ian Barbour - http://www.flickr.com/people/barbourians/
- 18. Twitter: helicopter crashat 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 Recommendations Lada Adamic http://www.foodandtechconnect.com/siteold/2011/11/28/mining-allrecipes-coms-ingredient-networks-for-recipe-recommendations/
- 20. Facebook: all friendrelationships Paul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919
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- 22. Within the institutionalcontexts Course data / LMS
- 23. 1st-year course combinations HS HUM COM SCI EBE
- 24. Maths and MathsLiteracy UCT Humanities students course combinations 26% to 50% Maths Lit More than 50% Maths Lit No Maths Lit 1% to 25% Maths Lit
- 25. Purdue University's CourseSignals • 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
- 27. Students’ use ofVula in a course Submission of assignments Polling of students Site visits Content accessed Chat room activity Sectioning of students
- 28. Sociogram of adiscussion forum Dawson (2010)
- 29. Words in chatsused by failing students
- 30. Words used byLecturers vs Students Marks; thanks; ‘Weiten’ – test; textbook Tut; author guys Week; pages Used more by Used more by Lecturers/tutors Students
- 31. Effective visualisations The successof a visualization is based on deep knowledge and care about the substance, and the quality, relevance and integrity of the content. Tufte (1981)
- 32. 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)
- 33. Future scenarios • Socialnetworks 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
- 34. 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
- 35. 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.
- 36. Font references • FatFontsby Miguel Nacenta, Uta Hinrichs and Sheelagh Carpendale. The area of each number is proportional to its value http://fatfonts.org Source: http://fatfonts.org