Learning Analyticsways of visualising educational data        Andrew Deacon Centre for Educational Technology     Universi...
Outline•   Understandings of learning analytics•   Data landscape of learning•   Toolsets and reproducible research•   Loo...
‘Analytics’ as a search-term                    Google Trends
Learning AnalyticsThe measurement, collection, analysisand reporting of data about learnersand their contexts, for purpose...
Age of Big DataSource: The Economist
What foundations fund• Bill & Melinda Gates Foundation  http://www.gatesfoundation.org• Kresge Foundation  http://www.kres...
Educational data landscape              Institutional                                      Individual          Institution...
Reproducible Research• Conducting analyses such that:  – code transforms raw data and meta-data  – code runs analyses on t...
Institutional
Purdue Universitys Course Signals• Early warning signs  provides intervention to  students who may not  be performing well...
Students’ use of Vula in a course                           Submission of                            assignmentsPolling of...
Sociogram of a discussion forum                      Dawson (2010)
Words in chats used by failing students
Words used by Lecturers vs Students                                    Marks;                                    thanks;‘W...
Beyond the Institution    Social Media / PLEs
Big breakthroughs happenwhen what is suddenlypossible meets what isdesperately necessary.                      Thomas Frie...
High profile MOOCs
Coursera free online course
Experiment:  Concept Mapping or Retrieval PracticeJ D Karpicke, J R Blunt Science 2011;331:772-775  Published by AAAS
If our aim is to understand people’sbehaviour rather than simply to recordit, we want to know about primarygroups, neighbo...
UCT and social media• Prominent links to:  – Facebook  – Flickr  – LinkedIn  – YouTube
Twitter: UCT chatter• Six months of data (April – Sept 2011)• Tweets including a UCT hashtag or text      #UCT, #Ikeys, Un...
Twitter: apps & locationsBlackberry     Twitter   Ubersocial   Others         17%                              Cell phones...
Twitter: viral #UCT6 months of tweets     Varsity Cup                          final                                     H...
Twitter: tweeter relationshipsSmall number offrequent tweeters1. Drama student  (162)2. UCT Radio  (132)3. Science student...
Flickr: helicopter crash at UCT         Ian Barbour - http://www.flickr.com/people/barbourians/
Twitter: helicopter crash at UCT• Crash or  hard-landing?• Media outlets getting  re-tweeted• Peak: 140 in 5 min          ...
Facebook: all friend relationshipsPaul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/4...
1st-year coursecombinations                   HS                   HUMCOM             SCI                         EBE
Correlation and causation• Correlation does not imply causation  – Covariation is a necessary but not a sufficient    cond...
Future scenarios• Research requiring   –   More detailed institutional data sets   –   Analysis including social media & P...
Tools usedTool       Example TAGS
Software references•   Gephi – network analysis, data collection•   NodeXL – network analysis, data collection•   TAGS – d...
Literature references• Baker, S.J.D., Yacef, K. (2009) The State of Educational Data Mining in 2009:  A Review and Future ...
Draws from a presentation at SAAIR 2011:“Visualising activity in learning networks usingopen data and educational analytic...
Learning Analytics - CET Seminar 2012
Learning Analytics - CET Seminar 2012
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Learning Analytics - CET Seminar 2012

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Introduction to the learning analytics and some learning analytics techniques anyone can use.

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Learning Analytics - CET Seminar 2012

  1. 1. Learning Analyticsways of visualising educational data Andrew Deacon Centre for Educational Technology University of Cape Town CET Seminar 2012
  2. 2. Outline• Understandings of learning analytics• Data landscape of learning• Toolsets and reproducible research• Looking for trends in large datasets• Visualizing data beyond dashboards• Future scenarios
  3. 3. ‘Analytics’ as a search-term Google Trends
  4. 4. Learning AnalyticsThe measurement, collection, analysisand reporting of data about learnersand their contexts, for purposes ofunderstanding and optimising learningand the environments in which itoccurs.Learning Analytics 2011 Conference, https://tekri.athabascau.ca/analytics
  5. 5. Age of Big DataSource: The Economist
  6. 6. What foundations fund• Bill & Melinda Gates Foundation http://www.gatesfoundation.org• Kresge Foundation http://www.kresge.org• Michael & Susan Dell Foundation http://www.msdf.org And what gets little support
  7. 7. Educational data landscape Institutional Individual Institutional data Social media Personal Learning & learning environments & social learning Environments (PLE)• ERP Systems• Historical performance data• Learning management system data• Libraries• School application data• Turnitin Reports• Demographics Data is Data is Data is • Accessible • Restricted • Almost unattainable • Can identify individuals • Difficult to link to • Difficult to link to individuals individuals
  8. 8. Reproducible Research• Conducting analyses such that: – code transforms raw data and meta-data – code runs analyses on this processed data – code incorporates this analyses into a report• Sharing allows other to: – confirm the correctness of the analyses – do analyses not reported by original researchers
  9. 9. Institutional
  10. 10. 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
  11. 11. Students’ use of Vula in a course Submission of assignmentsPolling ofstudents Site visits Content accessed Chat room activitySectioningof students
  12. 12. Sociogram of a discussion forum Dawson (2010)
  13. 13. Words in chats used by failing students
  14. 14. Words used by Lecturers vs Students Marks; thanks;‘Weiten’ – test; textbook Tut; author guys Week; pages Used more by Used more byLecturers/tutors Students
  15. 15. Beyond the Institution Social Media / PLEs
  16. 16. Big breakthroughs happenwhen what is suddenlypossible meets what isdesperately necessary. Thomas Friedman New York Times, 15 May 2012
  17. 17. High profile MOOCs
  18. 18. Coursera free online course
  19. 19. Experiment: Concept Mapping or Retrieval PracticeJ D Karpicke, J R Blunt Science 2011;331:772-775 Published by AAAS
  20. 20. 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
  21. 21. UCT and social media• Prominent links to: – Facebook – Flickr – LinkedIn – YouTube
  22. 22. Twitter: UCT chatter• Six months of data (April – Sept 2011)• Tweets including a UCT hashtag or text #UCT, #Ikeys, University of Cape Town, …• Attributes; how tweets are amplified• Just over 5,000 tweets Cannot capture every tweet on the topic And some data cleaning required
  23. 23. Twitter: apps & locationsBlackberry Twitter Ubersocial Others 17% Cell phones: 27% Blackberry Smartphone geo-location 20% 36% Cell phones
  24. 24. Twitter: viral #UCT6 months of tweets Varsity Cup final Helicopter crash
  25. 25. Twitter: tweeter relationshipsSmall number offrequent tweeters1. Drama student (162)2. UCT Radio (132)3. Science student (84)
  26. 26. Flickr: helicopter crash at UCT Ian Barbour - http://www.flickr.com/people/barbourians/
  27. 27. Twitter: helicopter crash at UCT• Crash or hard-landing?• Media outlets getting re-tweeted• Peak: 140 in 5 min 2 hours after the event
  28. 28. Facebook: all friend relationshipsPaul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919
  29. 29. 1st-year coursecombinations HS HUMCOM SCI EBE
  30. 30. 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)
  31. 31. Future scenarios• Research requiring – More detailed institutional data sets – Analysis including social media & PLE data – Modelling and predicting success – Reproducible research – Ethical considerations• Visualisations and multivariant analysis – Deepening understandings – Making information more accessible
  32. 32. Tools usedTool Example TAGS
  33. 33. Software references• Gephi – network analysis, data collection• NodeXL – network analysis, data collection• TAGS – data collection (Google Doc)• Word cloud – R package (wordcloud)• Geo-location map – R package (RgoogleMaps)• Excel – spreadsheet, charts• SPSS – statistical analysis, graphs
  34. 34. Literature references• Baker, S.J.D., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions: http://www.educationaldatamining.org/JEDM/images/articles/vol1/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). http://www.slideshare.net/BCcampus/learning-analytics-fritz• Kirschner, P.A., Karpinski, A.C. (2010) Facebook and academic performance. Computers in Human Behavior, 26: 1237-1245.• 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.
  35. 35. Draws from a presentation at SAAIR 2011:“Visualising activity in learning networks usingopen data and educational analytics”by Andrew Deacon and Michael Paskevicius

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