Presentation at ascilite learning analytics special interest group meeting at ascilite 2015. For more information, see video: https://youtu.be/LjxSZWtWxT0

1. The University of Sydney Page 1
A personalized and cross-
institutional approach to
connect students with staff
through customizable
analytics
Dr Danny Liu
Prof Adam Bridgeman
Dr Sophia Barnes
Cassie Khamis
Ana Munro
A/Prof Charlotte Taylor
Zinnia Sahukar

2. The University of Sydney Page 2
Attrition and engagement issues at Sydney
– Impacts of attrition are significant
– For students: personal and financial impact potentially devastating
– For the institution: ~$7 million/year lost from attrition
– Early attrition analysis1 identifies indicators of risk for
first-year students
– Balancing commitments, engaging online and in class, stress and
anxiety, lack of connection
– Best practice research identifies early intervention
strategies and critical points for decision making2
– Need to identify students early and accurately
– Need to connect with them as individuals, through multiple means
1 Adams, T., Banks, M., Davis, D. & Dickson, J. (2010). The Hobsons retention project. Melbourne: Tony Adams and Associates.
2 Wilson, K. (2009). The impact of institutional, programmatic and personal interventions on an effective and sustainable first year student experience. In 12th First Year in Higher Education
Conference 2009, Townsville.

3. The University of Sydney Page 3
Disconnected students, disconnected data
Jirka Matousek https://flic.kr/p/dREEsP CC-BY-2.0
Large cohorts
Generalised
Inefficient
Disconnected
data sources
Disconnected
students
Lagging
indicators
Personalised
Targeted
Just in time
Customisable
sources
Connecting staff
and students
Leading
indicators

4. The University of Sydney Page 4
Data that matter – learner engagement
Central
database
Attendance1
Interim
grades2
LMS
metrics3
Collaborative
interactions4
Other data
as needed
1 Massingham P, Herrington T (2006) Journal of University Teaching &
Learning Practice, 3, 82-103.
2 Clow D (2012) The learning analytics cycle: closing the loop
effectively. In Proceedings 2nd International Conference on Learning
Analytics & Knowledge, Vancouver, BC, Canada, April-May 2012.
3 Dawson S, McWilliam E, Tan J (2008) Teaching Smarter: How mining
ICT data can inform and improve learning and teaching practice. In
Proceedings, ASCILITE: Melbourne, Australia, December 2008.
4 Macfadyen L, Dawson S (2010) Computers & Education, 54, 588-599.
Student
Relationship
Engagement
System
– Engagement data
– Flexibility

5. The University of Sydney Page 5
Data to the people (and from, and for)
– At teachers’ fingertips to augment human
interaction and support
Attendance

6. The University of Sydney Page 6
Lowering barriers to data collection
– Easy collection and collation of grades & other records
Interim
grades

7. The University of Sydney Page 7
Fully customisable data
– Flexible, customisable database
Interim
grades
LMS
metrics
Collaborative
interactions
Other data
as needed Student
Relationship
Engagement
System

8. The University of Sydney Page 8
Personalising connections with students
– Instructors build
customised filters
– Flexible
– Targeted
– Benefits
– Customisable to specific
needs1
– Efficient – key data in one
place, operating at scale
– Look across programs
Student
Relationship
Engagement
System
1 Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84.

9. The University of Sydney Page 9
Personalising connections with students
– Instructors build
customised messages
– Personalised
– Multi-channel
– Benefits
– Connect staff and all
students (not just at-risk)
– Report can be
forwarded to Track &
Connect team in Student
Services
Student
Relationship
Engagement
System

10. The University of Sydney Page 10
Organic institutional adoption
0
5000
10000
15000
20000
25000
0
10
20
30
40
50
60
70
2012 2013 2014 2015
Numberofstudents
Numberofunitsorschools
Number of units Number of schools Number of students
Pilot

11. The University of Sydney Page 11
Tracking and connecting with at-risk students
Data
SRES
Unit
coordinator
Lists and
scripts
Student
callers
Call
summaries
Students
Track&Connect
Faculty
Messages

12. The University of Sydney Page 12
Sustained impact in large enrolment units
– Sustained reductions in drop-out and failure rates in units with
traditionally high rates
Discontinued
Failed
Passed
SRES +
Track & Connect
SRES +
Track & Connect
Arts unit
Science unit

13. The University of Sydney Page 13
Improvement in all student outcomes
– Sustained improvements in pass rates and increasing proportion
of higher merit grades by targeting students, resolving issues
SRES +
Track & Connect
SRES +
Track & Connect
F
P
C
D
HD
Arts unit
Science unit

14. The University of Sydney Page 14
Learner and unit coordinator perspectives
“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."
“[Track & Connect] is probably one of the greatest things
that the University has done.”
“We very much welcome this type of union of academic
teaching and student support.”
"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."

15. The University of Sydney Page 15
Thanks to…
– Our students
– Adventurous unit & program coordinators
– Student services team
– Cassie Khamis
– Dr Sophia Barnes
– Ana Munro
– Faculty team
– Prof Adam Bridgeman
– A/Prof Charlotte Taylor
– Zinnia Sahukar
danny.liu@sydney.edu.au
@dannydotliu