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Visualising activity in learning networks using open data and educational analytics


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Presented on 13 October 2011 at the Southern African Association for Institutional Research Forum in Cape Town.

As more student academic activities involve both institutional and social networks, educational analysts are needing to investigate ways in which this data can be collected and interpreted to enhance learning experiences. Data recorded as students explore personal learning environments is most often not accessible or incomplete. Here we explore some of the approaches that exist to use these social networking platforms along with information from the learning management system and academic records. Combining and analysing this data has allowed us to create a number of interesting visualizations exposing patterns which would have been impossible to glean from looking at the data alone. In an age of data abundance we reflect on using some of these new measures in relation to improving learning design, increasing academic responsiveness and enhanced student experiences.

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Visualising activity in learning networks using open data and educational analytics

  1. 1. Visualising activity in learning networks using open data and educational analytics Andrew Deacon & Michael PaskeviciusCentre for Educational Technology, University of Cape Town Southern African Association for Institutional Research (SAAIR) Forum 2011
  2. 2. Centre for Educational Technology within the Centre for Higher Education Development– Michael Paskevicius (Learning Technologist) • Interested in social media and open education • Previously MIO at Polytechnic of Namibia– Andrew Deacon (Learning Designer) • Experienced learning designer • Significant experience analysing assessment
  3. 3. Agenda• Definition of educational analytics• Explore the data landscape of institutional learning environments, personal learning environments and social media• Learning analytics – approaches & challenges at the University of Cape Town (Michael)• Visualizing complex data – beyond univariant dashboards (Andrew)• Available toolsets and concluding thoughts
  4. 4. An age of data• Massive increase in data storage capability• What about data collected within learning environments? Source: The Economist Source: Telegraph Source: Deloitte Consulting
  5. 5. Educational analytics• The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. (Learning Analytics 2011 Conference site:• Exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in. (Baker & Yacef, 2009)• Academic analytics can be used to profile and even predict students who may be at risk, by analysing demographic and performance data of former students. (Fritz, 2011)
  6. 6. Educational analytics data landscape Social media Institutional learning Personal learning The social web environments environments (PLE)• ERP Systems• Historical performance data• Learning management system data• Libraries• School application data• Turnitin Reports• Demographics Attributes Attributes Attributes • Owned data • External data • External data • Accessible • Mostly difficult to obtain if at all • Mostly difficult to obtain if at all • Found in various databases • Difficult to connect to • Difficult to connect to institutional data institutional data • Perhaps not academic at all
  7. 7. If our aim is to understand people’sbehaviour rather than simply to record it,we want to know about primary groups,neighbourhoods, organizations, socialcircles, and communities; about interaction,communication, role expectations, andsocial control.Allen Barton, 1968 cited in Freeman, C. (2004)
  8. 8. Tools usedTool name Example Twitteralytics
  9. 9. Data sources Web and activity log scraping • How do people connect with each other in collaborative academic environments? • What types of interaction occur in a forum or chat room discussion? Social network analysisSource: CC BY-SA 3.0 • What are people saying about our university in social networks? • How are students related within social networks? Extract method: • Query select data via API or script (Python, PHP, screen- scraping programs) • Group by hashtags, groups, users, topics, keywords • Often requires addition of semantic understanding (and associated documentation)
  10. 10. Institutional Learning Environments
  11. 11. Starting point: UCT Learning Management System
  12. 12. How and when do students use the learning management system? Submission of assignmentsPolling ofstudents Site visits Content accessed Chat room activitySectioningof students
  13. 13. Does student LMS activity correlate to course grade?
  14. 14. How do students and academics engage in aAcademics course chat room?and support staff Days in which chat occurred Chat messages linked to day of occurrence Students
  15. 15. What do students who drop the course chat mostly about?
  16. 16. How do students engage with academics in a chat room?Said more by Said more by educators students
  17. 17. PLEs / Social Media
  18. 18. Exploratory data analysis• Getting actual social media data (vs surveys / aggregated data)• Usage and trends Confirm what happened• Relationships Explain how things are connected• Comparisons Serendipity as new questions arise
  19. 19. UCT and social media• Prominent links to: – Flickr – YouTube – Facebook – LinkedIn
  20. 20. Twitter: student surveyWould use on my cell phone YesSMS 99%Webmail 94%Facebook 92%Wikipedia 90%Library journals 85%Flickr, YouTube 74%Google Docs 63%Skype 61%Twitter 26% Vula student survey, 2010 data set
  21. 21. Twitter: UCT chatter• Six months of data (April – Sept 2011)• Tweets including a UCT hashtag #UCT, #Ikeys, …• Attributes; how tweets are amplified• Just over 5,000 tweets• Cannot capture everything referring to something• Clean dataset to exclude other uses of hashtags
  22. 22. Twitter: apps & locationsBlackberry Twitter Ubersocial Others 17% Cell phones: 27% Blackberry Smartphone geo-location 20% 36% Cell phones
  23. 23. Twitter: viral #UCT6 months of tweets Varsity Cup final Helicopter crash
  24. 24. Twitter: tweeter relationshipsSmall number offrequent tweeters1. Drama student (162)2. UCT Radio (132)3. Science student (84)
  25. 25. Flickr: helicopter crash at UCT Ian Barbour -
  26. 26. Twitter: helicopter crash at UCT• Crash or hard-landing?• Media outlets getting re-tweeted• Peak: 140 in 5 min 2 hours after the event
  27. 27. Facebook: all friend relationshipsPaul Butler
  28. 28. UCT: first-year coursesPsychology and Economicscourses have students registeredfor the largest number of other course(node size is the number of edges)
  29. 29. Data acquisition & preparation• Social media data challenges – Tools and data APIs changing – Being commercialised (and throttled) – Data cleaning required
  30. 30. Correlation and causation• Correlation does not imply causation – Covariation is a necessary but not sufficient condition for causality – Correlation is not causation (could be a hint)
  31. 31. Conclusions• Exploring emerging data sources – Combined institutional data sets – Acknowledge Personal Learning Environments – Highly fragmented social media data – Collectively enrich existing information• Visualisations and multivariant analysis – New exploratory tools – Making information more accessible
  32. 32. Literature references• Baker, S.J.D., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions: /issue1/JEDMVol1Issue1_BakerYacef.pdf• 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). fritz• Kirschner, P.A., Karpinski, A.C. (2010) Facebook and academic performance. Computers in Human Behavior, 26: 1237-1245.
  33. 33. Software references• Gephi – network analysis, data collection• NodeXL – network analysis, data collection• Twitteralytics – data collection (Google Doc)• Word cloud – R package (wordcloud)• Geo-location map – R package (RgoogleMaps)• Excel – spreadsheet, charts• SPSS – statistical analysis, graphs