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Learning Analytics - L&D Community of Practice 2012
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Learning Analytics - L&D Community of Practice 2012


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Learning analytics to inform learning design.

Learning analytics to inform learning design.

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  • 1. Learning Analytics explorations of learning data Andrew Deacon Centre for Educational Technology University of Cape TownLearning and Development community of practice – 9 Nov 2012
  • 2. Outline• Understandings of learning analytics• Instructional design case• Data landscape in learning organizations• Trends within the organization context• Connections with external contexts• Future scenarios
  • 3. Search-term: ‘Analytics’ Google Trends
  • 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,
  • 5. Age of Big DataSource: The Economist
  • 6. An Instructional Design Case
  • 7. Merrill on IDInstruction involves directing students toappropriate learning activities;guiding students to appropriate knowledge;helping students rehearse, encode, andprocess information;monitoring student performance;and providing feedback as to theappropriateness of the student’s learningactivities and practice performance.
  • 8. NewsScripts: Scriptwriting exercise 1) Watch footage, note 2) Write script following 3) Take note of significant details and TV news writing feedback on research online. conventions. some issues.
  • 9. NewsScripts: Nit-picking bot• Rather than lots of instructions – that students would not follow anyway• Provide feedback on script – Check script length e.g., read at 3 words per second – Flag words editors avoid e.g., never use: ‘As the footage shows’ – Emphasise active voice e.g., avoid: ‘were’
  • 10. NewsScripts: Context• 2nd year UCT Media Studies course• Linked to lectures on media writing genres• 250 students• 2h to complete 180-word script• 10% of mark• Used since 2001 (with gaps)
  • 11. Questions for ID• Why is it so difficult to entrench such learning interventions in university curricula?• Curriculum integration tensions (relevance) – Media writing & Essay writing – Media production & Theory & critique – Online teaching spaces & Traditional modes
  • 12. 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
  • 13. Within the institutional contexts
  • 14. 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:
  • 15. Students’ use of Vula in a course Submission of assignmentsPolling ofstudents Site visits Content accessed Chat room activitySectioningof students
  • 16. Sociogram of a discussion forum Dawson (2010)
  • 17. Words in chats used by failing students
  • 18. Words used by Lecturers vs Students Marks; thanks;‘Weiten’ – test; textbook Tut; author guys Week; pages Used more by Used more byLecturers/tutors Students
  • 19. Predicting success Chemistry – 1st year courseNBT - National Benchmark Tests
  • 20. Predicting success
  • 21. Predicting success
  • 22. Predicting success
  • 23. Beyond the institution context Social Media / PLEs / CoP
  • 24. Big breakthroughs happenwhen what is suddenlypossible meets what isdesperately necessary. Thomas Friedman New York Times, 15 May 2012
  • 25. High profile MOOCs
  • 26. Coursera open online course
  • 27. Coursera open online course• Gamification course – 81,000 registrations – 8,280 received certificates (10%)• Participation – 20,000 forum posts – 187,000 peer evaluations by 13,000 students – Facebook group: 3,400 members – Twitter: > 2,700 tweets #gamification12
  • 28. Gamification course participation100%80% Registers60% Watchers Submitters40% Writers Certificate20% 0%
  • 29. Coursera: learning from videos Concept Mapping or Retrieval PracticeJ D Karpicke, J R Blunt Science 2011;331:772-775 Published by AAAS
  • 30. 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
  • 31. UCT and social media• Prominent links to: – Facebook – Flickr – LinkedIn – YouTube
  • 32. 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
  • 33. Twitter: apps & locationsBlackberry Twitter Ubersocial Others 17% Blackberry 27% Smartphone geo-location 20% 36% Cell phones
  • 34. Twitter: tweeter relationshipsSmall number offrequent tweeters1. Drama student (162)2. UCT Radio (132)3. Science student (84)
  • 35. Twitter: viral #UCT6 months of tweets Varsity Cup final Helicopter crash
  • 36. Flickr: helicopter crash at UCT Ian Barbour -
  • 37. 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
  • 38. Facebook: all friend relationshipsPaul Butler
  • 39. 1st-year coursecombinations HS HUMCOM SCI EBE
  • 40. Effective visualisationsThe success of a visualization is basedon deep knowledge and care aboutthe substance, and thequality, relevance and integrity of thecontent. (Tufte 1981)
  • 41. 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)
  • 42. Future scenarios• Learning analytics for educational research – Instructional data within wider contexts – Social media & PLE outside formal contexts – Modelling and predicting success – Supporting intervention opportunities – Reproducible research – Ethical considerations• Learning analytics for visualisations – Presenting data in engaging forms – Relating several variables
  • 43. 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
  • 44. 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• 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.• 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
  • 45. FatFonts references• by Miguel Nacenta, Uta Hinrichs, and Sheelagh Carpendale• Area of each number is exactly proportional to its value - Source: