This document summarizes a workshop on where learning analytics meets learning design. The workshop included introductions, discussions of learning design and learning analytics, and an activity to identify how analytics could support learning designs. Learning design involves describing teaching and learning activities and has various frameworks. Learning analytics involves collecting and analyzing data about learners to understand and optimize learning. The workshop discussed what types of data are available from learning management systems and how patterns in that data can provide insights about learners' motivations, performance, and social interactions that can then be used to personalize support and improve learning designs.

1. Where Learning Analytics meets
Learning Design
Lori Lockyer, University of Wollongong
llockyer@uow.edu.au
Shane Dawson, University of British Columbia
shane.dawson@ubc.ca

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. Introductions
• Who you are?
• Where you are?
• What you do?
• Where are you with learning designs and/or
learning analytics

4. Learning Design
Concept evolved soon after the „learning
object‟ concept
Building on ideas of packaging, describing,
storing, sharing, reusability
Who, what, where, when, how of teaching
and learning

5. Internationally
Terminology: pedagogical models,
pedagogical patterns…
IMSLD http://www.imsglobal.org/learningdesign/
Different frameworks to describe learning
designs
Different tools and strategies to create and
adapt learning designs

7. learningdesigns.uow.edu.au (2000)

8. Based on: Oliver, R. (1999). Exploring strategies for online teaching and learning. Distance
Education, 20(2), 240-254.

10. The Learning Design Journey
Language for resources, activities, supports
Tools to translate between teacher language,
IMSLD, and LMSs
How do teachers use and interpret LDs
How can LD be used as for review/reflection
LD web 2.0 tool - http://needle.uow.edu.au/ldt/
How do teachers design?

11. How do teachers design?
• Iterative process
• Macro to micro views
• Differential considerations and constraints
• Inspired by others – regardless of discipline

12. Activity:
Interpreting Designs…

13. Learning Analytics Overview
1. Learning analytics
2. What data?
3. Case Studies
4. Aligning data with design

14. Background
Learning Analytics:
is the collection, collation, analysis and
reporting of data about learners and their
contexts, for the purposes of understanding
and optimizing learning

15. Do not under estimate
Most important educational movement
of the last 100 years.
Siemens, G. (2011). An introduction to the role of learning and
knowledge analytics in education. Invited presentation, Lisbon,
Learning Analytics

16. Background
Ed theory, Ed practice, SNA, Data mining,
Machine learning, semantic, data
visualisations, psychology (social,
cognitive, organisational)

17. Large data sets – mine for
trends/ patterns or anomalies.
• Creatures of habit (Study,
communication, search
patterns, networks, credit
card security, Movies)
What do patterns indicate and
what do changes in habit
indicate?

18. Activity
What data do you have access to?
• Online/Offline
• Individual/Course/ Program/
Faculty or institutional
• Psych based surveys?

19. Performance and Behaviour
Relationship between
grades and online
activity
What data?

20. Performance and Behaviour
Yes there is relationship!
Frequency of logins
Discussion activity
Influenced by context

21. An online world
Concerns with Attrition
• Motivation the issue
• Achievement orientations

22. Achievement orientations
Learning or performing
• Carol Dweck
• Jen Tan
I just failed my essay. Maybe it was a
mistake to text it to my English teacher

23. Study
Grad School of Medicine
• PBL centric
• Well integrated OLE

24. Study
OLE
• LMS – clinical cases, content, virtual patients
• Learning forum
• Admin forum

25. Study
Learning analytics
- Tracking data

26. Study
Learning 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

27. Results
Where to post?
• LG = Learning and sharing forum
• PG = Admin forum

28. Predicting motivation
High users of Admin
forum - performance
driven
High users of learning
forum – learning driven

29. So what? Predicting motivation
Monitor student participation for
understanding motivation –
reduce attrition?
Better develop and personalise
student learning support
Context related

30. What about social networks?
Student social network data as measures of student
learning

31. Node = student
Ties = interaction between 2 students

32. SNAPP
Social Networks Adapting Pedagogical Practice

33. SNAPP

34. SNAPP

35. SNAPP
Disconnected students
Instructor – subject
aim to generate a
student community
through the online
discussion activities

36. SNAPP
Instructor – using
SNAPP over the
course
Signature pedagogy?

37. Indicators of Academic Success
Engagement in
discussion forums

38. Individual Networks
Disconnected students
Network density

39. Individual Networks
Ego-networks
• Top 10%
• Bottom 10%

40. Individual Networks
Low 10% network example
Student with a
passing grade
Top 10% student located in network

41. Individual Networks
High 10% network example
(>90% grade score)
Students with a
grade >75% < 90%
Low 10% student located in network

42. Teacher presence
• Staff intervention
• High – 70% of networks
• Low – 10% of networks
• Why?
• Developing community
Blind pursuit of community
Modify context

43. Learning analytics – Creativity?
Monitoring online networks –
not just participation
- Academic performance
- Networking skills
- Communication
- Creative capacity

44. 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.

45. Learning analytics – Creativity?
Bridging structural holes

46. Learning analytics – Creativity?
Student cognitive playfulness and creativity
Mapping network centrality measures
- Betweenness
- Closeness
- Degrees

47. Learning analytics – Creativity?
Very complex

48. Bringing design and analytics
together
Analytics to inform design decisions
Just-in-time analytics to understand
learner activity and experience during
implementation
Recommendations for action
Analytics for post-implementation
reflection and revision

49. Activity
Learning Design:
What are the core interaction types and why?
(engagement, community, independent study,
knowledge recall)
What data can you access? Where is this
located?
What data will inform these?
What patterns do you anticipate?

50. Thank you – Questions?