Learning analytics aims to collect, analyze, and report on student data to understand and optimize learning. It draws on fields like education theory, social network analysis, and machine learning. Current analytics focus on retention and early intervention but are progressing towards predictive, adaptive, and recommender systems. Tools like SNAPP provide lightweight social network analysis to help interpret student interaction patterns and evaluate teaching interventions. However, ensuring privacy and developing analytics with appropriate educational context remain ongoing challenges.

1. Learning analytics:
Building evidence based
practice
Assoc. Prof Shane Dawson
Deputy Director LTU
University of South Australia

2. What about today?
• Current state of play
• What analytics are in place?
• What questions and data?
• Patterns of data – importance of context
• Analysis tools - SNA
• Curriculum analytics
• Privacy/ ethics
• Questions, concerns or issues

3. Where are LA?
Peak of inflated expectations
Slope of enlightenment
Trough of disillusionment
Technology trigger
Plateau of productivity

4. Learning Analytics
What are learning analytics?

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

6. Ed theory, Ed practice, SNA, Data
mining, Machine learning, semantic,
data visualisations, sense-making,
psychology (social, cognitive,
organisational), learning sciences
Learning Analytics

7. Creatures of habit (Study, communication, search
patterns, networks, credit card security, Movies)
What do patterns indicate and what do changes in
habit indicate?
Learning Analytics

8. • 5 Billion mobile phones (2010)
• 30 Billion content shared on facebook
• $600 drive to store all of the worlds music
• 60% increase in operating margin for retailers
using big data
Its accessible, cheap and critical
Big data
Manyika, J., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity:
McKinsey Global Institute

9. Examples
Develop a predictive algorithm to identify who will be
admitted to a hospital using historical claims data
Kaggle: connect with
data scientists

10. Examples
Loyalty programs
Black Box trading

11. Target coupons inform father of daughter's
pregnancy
Examples

12. “Data is the new oil”
Higher education:
• Lots of isolated work targeting attrition. Few
large enterprise egs.
• Commercial – IBM, D2L S3, BB analytics
Why?
Where is LA?

13. • “High potential but low mindset”
• Target rapid returns – students at risk.
• Predictive Analytics Research Framework:
• What data? Lets define terms
Potential is there
Manyika, J., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity:
McKinsey Global Institute

14. Education Examples

15. Education Examples

16. Education - Purdue

17. Education - UMBC

18. Education – Fort Hays State Uni

19. What questions?
What questions are learning analytics attempting
to address?
What analytics work is being undertaken at your
institution?
How far has this progressed?

20. Pass/Fail, Retention
Concept understanding
Learning motivation/ engagement
Learning dispositions
Graduate qualities
Curriculum pathways
Learning experience
Satisfaction, community
Questions explored

21. Rapidly moving beyond simple reports, attrition
and student learning support measures
TO -
Predictive, Adaptive and Recommender states
• Akin to – iTunes Genius, Amazon, Gmail
Future

22. Future

23. Example Knewton:
2012 - 500,000 students
2013 - 5 million students
2014 - 15 million students
1 million points of data per student.
Curriculum and activities modified based on the
individual student.
Future

24. Emotions/ face tracking
Future
Confusion Engaged
Frustrated
Activity
modified
Continuous
state of
challenge

25. In Australia:
• Largely focused on retention and early
intervention
Examples:
• UniSA – ESAP
• QUT – student success and retention
• CQU – indicators project
• UTS – data intensive university
Potential is there

26. What questions?
What questions do you want LA to answer?
What is the scope – e.g. institution / program or
course?

27. What questions?
Attrition
Student failure
Teaching quality
Student learning
Graduate qualities
Quality assurance

28. What data?
What data do you have access to?

29. What data?
LMS activity
Student grades and assessment activity
Student demographics
Prior learning experiences
Other technologies – clickers, lecture recordings
Course exp survey/ Student evaluations
Course progression
Institutional surveys
Library
Wireless, security camera, occupancy
Social media
Sentiment analysis

30. Who are the key stakeholders?
Who has access to this data?
Governance/ buy in?
Additional data sets required?

31. Analysis
How will you analyse this data?
Who can analyse this data?
What tools?

32. Its all about interpretation
What is the interpretation of the data?
Example patterns

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Its all about interpretation
Student login frequency avg / week

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Class average

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High performing student

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Typical login engagement pattern

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-4 -2 0 2 4 6 8 10 12
Grades vs Time of first login (0 = course start)

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-4 -2 0 2 4 6 8 10 12

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41. What are the predictors of failure and
retention?
• Prior grades
• Low SE
• First in family
• Study load
• Engagement online (time of login/
discussion activity)
Risk assessment

42. What patterns do you expect?
LMS activity
Class interaction
Assessment
Qualitative
Survey
Networks
What patterns?

43. • Context matters!
Be critical

44. Analytic techniques SNA
“single most potent source of influence”
Astin, A. (1993). What matters in college: Four critical years revisited.
San Francisco: Jossey-Bass.
Student Networks

45. Analytic techniques SNA
Social network building blocks:
Actors (nodes)
Relations (lines)
Network (graph)

46. Analytic techniques SNA
Interpreting visualisations

47. SNAPP
• Social Networks Adapting Pedagogical Practice
• Focus on student relationships (learning
networks)
• Simple visualizations to assist with
interpretation and evaluate impact of activities
• Lightweight analytics tool
• Bookmarklet
• Rapid and easy dissemination
Bakharia, A., & Dawson, S. (2011). SNAPP: a bird's-eye view of temporal participant interaction.
Paper presented at the 1st International Conference on Learning Analytics and Knowledge, Banff,
Alberta, Canada

48. SNAPP

50. SNAPP
• No need to access database
• No need for Admin rights (installation of a
bookmark)
• Broad accessibility and compatibility
• Fast delivery mechanisms – focus on simplicity

51. • Forum A • Forum B
14 messages posted by 4 participants
Measuring Interaction

52. • Forum A • Forum B
Measuring Interaction

53. Seeing networks in action
Forum 1 Forum 2

54. Seeing networks in action
Forum 1 Forum 2
Visualisations aid
interpretation

55. SNAPP
• http://www.snappvis.org/
• http://w3.unisa.edu.au/ltu/snapp.html
• http://www.moodle.org/
<community tab < forums

56. Other tools
Jigsaw: visual analytics for
documents
http://www.cc.gatech.edu/gvu/ii/jigsaw/
Netlytic: text & SNA for
Twitter, Youtube, blogs, etc.
Gephi.org

57. Other tools

58. Network examples
Monitoring online networks
- Informed decisions for improving learning
design
- Evaluate impact of implemented
activities

59. Instructor
Disconnected students
Is this a learning community?
Facilitator Centric

60. Instructor
Facilitator Centric

61. Ego-Networks
Ego-networks
• Top 10%
• Bottom 10%

62. Low Performer
Top 10% student located in network
Student with a
passing grade

63. High Performer
Low 10% student located in network
Students with a
grade >75% < 90%

64. Teaching Presence
• Staff intervention
• High – 70% of networks
• Low – 10% of networks
• Why?
• The pursuit of community
Dawson, S. (2006). Online forum discussion interactions as an indicator of student community.
Australasian Journal of Educational Technology, 22(4), 495-510.
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.

65. Curriculum analytics
Need context in order to move from predictions to
recommender systems
Lecture, seminar, group
work, community,
online, hybrid, blended
What teaching model?
What outcomes?

66. Curriculum analytics
Curriculum networks – dominant
pathways

67. Curriculum analytics
What outcomes, what experiences?
A FED
C
B

71. Curriculum analytics
What outcomes, what experiences?
Assessment
Learning outcomes
Learning experiences
Graduate attributes
Automated
portfolio/
Learning
Relationship

72. Privacy and ethics
• Who “owns” data?
• Are analytics an intrusion of privacy?
• If we can identify students at risk is there an
obligation to intervene?

73. Thank you
Questions?
Shane.dawson@unisa.edu.au
Events:
A-LASI
(http://www.solaresearch.org/events/a-lasi/)
LAK14
(http://lak14indy.wordpress.com/)