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The University of Sydney Page 1
Empowering instructors through customizable collection and analyses of actionable information
@dannydotliu
The University of Sydney Page 2
Four tensions of learning analytics
Research Teaching
Top-down Bottom-up
Human Computer
Macro Micro
Data & algorithmsStudents & teachers
Massified & aggregate Contextualised
Practical & actionableTheoretical
Institutional Bespoke
The University of Sydney Page 3
The realities of learning and teaching at scale
Research Teaching
Top-down Bottom-up
Human Computer
Macro Micro
Data & algorithmsStudents & teachers
Massified & aggregate Contextualised
Practical & actionableTheoretical
Institutional Bespoke
The University of Sydney Page 4
AttendanceInterim grades
LMS metrics, other
3rd party data
The Student Relationship Engagement System
The University of Sydney Page 5
Personalising connections with students
– Empowering staff
– Flexible & intuitive
– Targeted and personalised
– Multi-channel
– Benefits
– Highly customisable
– Efficient – key data in one place, operating at scale
– Connect staff and all students (not just at-risk)
The University of Sydney Page 6
Student outcomes
Discontinued
Failed
Passed
F
P
C
D
HD
1st year arts unit ~500 enrolment 1st year science unit ~1000 enrolment
“Many thanks Adam. Yes, things are going much better this semester. I really appreciate how you keep in contact and keep an eye on us. It's such a big class, I don't know how you do it.”
“Just to let you know that your emails really helped me survive last semester. I never realised how big a change it would be from school.”
The University of Sydney Page 7
Co-evolution of the SRES
– Organic adoption by academics
– Co-evolution of capabilities
0
5000
10000
15000
20000
25000
0
10
20
30
40
50
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70
2012 2013 2014 2015
Num
ber
of
student
s
Num
ber
of
units
or
scho
ols
Number of units Number of schools Number of students
Pilot
EWS
introduced
EWS
integrated
New
analyses
More data
types
Data
import
The University of Sydney Page 8
Learning analytics by stealth?
– Co-evolving capabilities and competencies around data-driven pedagogy and curriculum
The University of Sydney Page 9
Practical learning analytics
Research Teaching
Top-down Bottom-up
Human Computer
Macro Micro
Data & algorithmsStudents & teachers
Massified & aggregate Contextualised
Practical & actionableTheoretical
Institutional Bespoke
The University of Sydney Page 10
Lessons learned and next steps
– Looking back
– It was ugly but it worked
– Personalisation at scale
– Ease of use is important – does it save time?
– Attendance was a (unsurprisingly?) popular metric
– Everyone uses a customisable system differently
– Looking forward
– Trans-Tasman redevelopment effort
– Facilitate wider roll-out
– Research & evaluation of impact on students and staff
The University of Sydney Page 11
Adoption pipeline
Colvin et al. (2015) Student retention and learning analytics: A snapshot of Australian
practices and a framework for advancement. Office of Learning and Teaching, Sydney.
“First, implementers require an analytic tool or combination of tools that manage data
inputs and generate outputs in the form of actionable feedback… As these increasingly
meet the real needs of learners and educators the organisational uptake is accelerated.”
The University of Sydney Page 12
“Learning analytics is not an elixir for ineffective teaching, nor does it reveal an ideal pedagogy; instead, it provides data-driven tools or suggestions to help instructors make changes that can be measured in terms of student outcomes.”
Pistilli, M. D., Willis III, J. E., & Campbell, J. P. (2014). Analytics Through an Institutional Lens:
Definition, Theory, Design, and Impact. In Learning Analytics (pp. 79-102). Springer New York.
The University of Sydney Page 13
– Adam Bridgeman
– Charlotte Taylor
– Abelardo Pardo
– Kathryn Bartimote-Aufflick
– Melanie Nguyen
– Hong Dao Nguyen
– And many staff and students
@dannydotliu
It takes a village
