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
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
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
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?
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
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
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
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?
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
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
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
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
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?
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?