Lak12 learning designs and learning analytics workshop

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This workshop was presented at the Second International Conference on Learning Analytics and Knowledge (LAK12) by Lori Lockyer & Shane Dawson.

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Lak12 learning designs and learning analytics workshop

  1. 1. Where Learning Analytics meets Learning Design Lori Lockyer, University of Wollongong llockyer@uow.edu.auShane Dawson, University of British Columbia shane.dawson@ubc.ca
  2. 2. Workshop Overview• 1.00 – 1.15 : Introductions• 1.15 – 1.50: Learning Design• 1.50 – 2.30: Learning Analytics• 2.30 – 3.00: Break• 3.00 – 3.30: Identifying Analytics to support designs• 3.30 – 4.00: Group reports and Conclusion
  3. 3. Introductions• Who you are?• Where you are?• What you do?• Where are you with learning designs and/or learning analytics
  4. 4. Learning Design Concept evolved soon after the „learning object‟ conceptBuilding on ideas of packaging, describing, storing, sharing, reusabilityWho, what, where, when, how of teaching and learning
  5. 5. InternationallyTerminology: pedagogical models,pedagogical patterns…IMSLD http://www.imsglobal.org/learningdesign/Different frameworks to describe learningdesignsDifferent tools and strategies to create andadapt learning designs
  6. 6. learningdesigns.uow.edu.au (2000)
  7. 7. Based on: Oliver, R. (1999). Exploring strategies for online teaching and learning. DistanceEducation, 20(2), 240-254.
  8. 8. The Learning Design JourneyLanguage for resources, activities, supportsTools to translate between teacher language,IMSLD, and LMSsHow do teachers use and interpret LDsHow can LD be used as for review/reflectionLD web 2.0 tool - http://needle.uow.edu.au/ldt/How do teachers design?
  9. 9. How do teachers design?• Iterative process• Macro to micro views• Differential considerations and constraints• Inspired by others – regardless of discipline
  10. 10. Activity:Interpreting Designs…
  11. 11. Learning Analytics Overview 1. Learning analytics 2. What data? 3. Case Studies 4. Aligning data with design
  12. 12. BackgroundLearning Analytics:is the collection, collation, analysis andreporting of data about learners and theircontexts, for the purposes of understandingand optimizing learning
  13. 13. Do not under estimateMost important educational movementof the last 100 years. Siemens, G. (2011). An introduction to the role of learning and knowledge analytics in education. Invited presentation, Lisbon, Learning Analytics
  14. 14. Background Ed theory, Ed practice, SNA, Data mining, Machine learning, semantic, data visualisations, psychology (social, cognitive, organisational)
  15. 15. Large data sets – mine fortrends/ patterns or anomalies.• Creatures of habit (Study, communication, search patterns, networks, credit card security, Movies)What do patterns indicate andwhat do changes in habitindicate?
  16. 16. ActivityWhat data do you have access to?• Online/Offline• Individual/Course/ Program/ Faculty or institutional• Psych based surveys?
  17. 17. Performance and BehaviourRelationship betweengrades and onlineactivity What data?
  18. 18. Performance and BehaviourYes there is relationship! Frequency of logins Discussion activity Influenced by context
  19. 19. An online worldConcerns with Attrition• Motivation the issue • Achievement orientations
  20. 20. Achievement orientationsLearning or performing• Carol Dweck• Jen Tan I just failed my essay. Maybe it was a mistake to text it to my English teacher
  21. 21. StudyGrad School of Medicine• PBL centric• Well integrated OLE
  22. 22. StudyOLE• LMS – clinical cases, content, virtual patients• Learning forum• Admin forum
  23. 23. StudyLearning analytics- Tracking data
  24. 24. StudyLearning dispositions Learning analytics• 5 factors • Time online • Learning Goals • Forum postings • Performance Goals • Sessions • Personal • Content Innovativeness • Files • Cognitive playfulness – • SNA curiosity • Cognitive Playfulness - creativity
  25. 25. ResultsWhere to post?• LG = Learning and sharing forum• PG = Admin forum
  26. 26. Predicting motivationHigh users of Adminforum - performancedriven High users of learning forum – learning driven
  27. 27. So what? Predicting motivationMonitor student participation forunderstanding motivation –reduce attrition?Better develop and personalisestudent learning support Context related
  28. 28. What about social networks?Student social network data as measures of studentlearning
  29. 29. Node = studentTies = interaction between 2 students
  30. 30. SNAPPSocial Networks Adapting Pedagogical Practice
  31. 31. SNAPP
  32. 32. SNAPP
  33. 33. SNAPPDisconnected students Instructor – subject aim to generate a student community through the online discussion activities
  34. 34. SNAPP Instructor – using SNAPP over the course Signature pedagogy?
  35. 35. Indicators of Academic Success Engagement in discussion forums
  36. 36. Individual Networks Disconnected students Network density
  37. 37. Individual Networks Ego-networks • Top 10% • Bottom 10%
  38. 38. Individual Networks Low 10% network example Student with a passing gradeTop 10% student located in network
  39. 39. Individual Networks High 10% network example (>90% grade score) Students with a grade >75% < 90%Low 10% student located in network
  40. 40. Teacher presence• Staff intervention • High – 70% of networks • Low – 10% of networks • Why? • Developing community Blind pursuit of community Modify context
  41. 41. Learning analytics – Creativity?Monitoring online networks –not just participation- Academic performance- Networking skills- Communication- Creative capacity
  42. 42. Learning analytics – Creativity?Ronald Burt “…see early and more broadly”Translators of information Communication Problem solving Burt, R. (1992). Structural holes: The social structure of competition. Cambridge, Mass: Harvard University Press.
  43. 43. Learning analytics – Creativity?Bridging structural holes
  44. 44. Learning analytics – Creativity?Student cognitive playfulness and creativity Mapping network centrality measures - Betweenness - Closeness - Degrees
  45. 45. Learning analytics – Creativity? Very complex
  46. 46. Bringing design and analytics togetherAnalytics to inform design decisionsJust-in-time analytics to understandlearner activity and experience duringimplementationRecommendations for actionAnalytics for post-implementationreflection and revision
  47. 47. ActivityLearning Design:What are the core interaction types and why?(engagement, community, independent study,knowledge recall)What data can you access? Where is thislocated?What data will inform these?What patterns do you anticipate?
  48. 48. Thank you – Questions?

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