Welcome briefing for attendees at our regular CIC2 networking event, introducing them to the work of UTS:CIC: http://utscic.edu.au

1. UTS CRICOS PROVIDER CODE: 00099F
Welcome to the
CONNECTED INTELLIGENCE CENTRE
…human-centred analytics for societal learning
utscic.edu.au
Simon Buckingham Shum, Director
CIC2 • 17th June 2016

2. 2011
2011
Envisioning “the Data
Intensive University”
UTS-wide Forum to
consider the profound
implications of the data
revolution
UTS IS TAKING DATA AS AN ASSET VERY SERIOUSLY
The Data Intensive University
25 October 2011
A University-wide Forum regarding data analytics will be held on 21 November.
“a university that knows about data, regardless of its volume and
diversity. It knows about the use and reuse of data to better
inform teaching, learning and research, as well as understand
business, society and the university itself, how to learn about
and research data, how to store and curate it, and how to apply
and develop analytical tools.”
Today data is ubiquitous, constantly generated and logged as we go about our
daily lives. We are all both consumers and producers of data. To understand our
world, we need to understand data.

3. 2011
2011
Envisioning “the Data
Intensive University”
UTS-wide Forum to
consider the profound
implications of the data
revolution
UTS-WIDE FORUM AND DETAILED CONSULTATIONS

4. OUR CONTEXT
4
Large scale data and analytics
are pervading societal life
Big Data has deep societal implications
– good and bad – demanding informed debate
The future workforce…
How universities teach, research, operate —
and are assessed…
How to equip graduates for
“the age of complexity” (Stephen Hawking)

5. Algorithms are now the focus of huge debate in
policy, law, technology, social science and ethics
http://governingalgorithms.org

6. Institute for the Future
http://www.iftf.org/futureworkskills

7. utscic.edu.au
While analytics aggregate lower level data
& A.I. gradually automates lower order skills…

8. utscic.edu.au
Humans must move to the higher ground…
• Train data scientists to combine algorithmic intelligence
with the deep skills that won’t be automated (anytime soon)
• Deploy all the Educational & Data Science expertise we
have to cultivate the higher order graduate qualities
While analytics aggregate lower level data
& A.I. gradually automates lower order skills…

9. utscic.edu.au
Curiosity • Reflection • Argumentation • Creativity
Values • Epistemic Shifts • Analogical Reasoning…
While analytics aggregate lower level data
& A.I. gradually automates lower order skills…

10. utscic.edu.au

11. CIC’S MISSION
11
CIC catalyses the use of data and
analytics among UTS students,
educators, researchers and leaders
We teach human-centred data science •
design analytics tools for UTS • evaluate
these • disseminate internally and
globally
We aim to shape critical debate on big
data in education, and societal learning

12. IML
HELPS .
Library .
<your unit here?> .
PARTNERING ACROSS UTS
12
Faculties
&
Institutes
Student
Support
Units
CIC
Business
Units
PQU
ITD
eResearch
<your unit here?>
Teaching
& Learning
collaborations
Business
Education
FEIT
Health
Law
Science
<your faculty here?>
Research
collaborations
FEIT
DAB
Health
FASS
<your faculty here?>

13. 3 CHALLENGES WE’RE WORKING ON…
13
Expand the beneficiaries of
analytics as a common good
Deliver timely personalised
feedback on writing
Deliver timely personalised
feedback on teamwork
UTS Data Taskforces
Master of Data Science
& Innovation (MDSI)
Writing Analytics
for critical analysis and reflection.
Collaboration Analytics
from multimodal data streams.

14. CONVENING AN INTERNAL DATA CHALLENGE
(GRADUATE EMPLOYMENT RATES)

15. CONVENING AN INTERNAL DATA CHALLENGE
(GRADUATE EMPLOYMENT RATES)
AGS Dataset
Planning & Quality Unit
Student Services/Careers
The ‘clients’ owning the problem
DVC (Education)
CIC
Coordinator/
Broker
School of Math. Sciences
FEIT School of Software
Advanced Analytics Institute
Connected Intelligence Centre
Analytics Experts
TASKFORCE: Clarify dataset, potential contributions, rules of
the game, schedule, and operationalise the challenge in
quantifiable terms

17. A DISTINCTIVE APPROACH TO DATA SCIENCE
utscic.edu.au
machine learning • statistics • data curation • ethics • user experience • information science
visualization • narrative • social computing • learning analytics • project management
project-based learning • authentic assessment
regular, meaningful employer engagement

18. https://utscic.edu.au/mdsi-students-shine-again-in-nsw-gov-data-challengeMDSI HAS MAINSTREAMED DATA HACKATHONS
From a few teams entering (+ winning) data
hackathons in 2015, to embedding day-long
events for all students in the core 2016
curriculum
Authentic learning valued by both students
and employers

19. uts.edu.au
Challenge 2
Deliver better feedback on writing to
students: 24/7 text analytics
Piloting in:
Engineering Law Accounting Pharmacy
Life Sciences Data Science Education
Graduate Research School

20. Highlighted sentences are colour-
coded according to their broad type
Sentences have Function Keys signalling where
an academic rhetorical move has been
recognised (e.g. a claim of Novelty )
AWA: ACADEMIC WRITING ANALYTICS
ANALYTICAL WRITING

21. REFLECTIVE WRITING (NURSING)
Applications for UTS researchers working with
text corpora, e.g. interview transcripts; literature
analysis; scenario planning?

22. UTS CIVIL LAW: STUDENT FEEDBACK
“takes the emotion out of having your work scrutinized” Respondent 12
“it was not embarrassing in the way that it can be when a tutor or marker
gives feedback” Student 7
“I realise now what descriptive writing is - the software had quite a bit to
say about my lack of justification - also true - pressed for time and
difficult circumstances have caused this for me in this instance - good to
see it sampled.” Respondent 9
“I definitely found it useful. It also made me realise that I tend to use bold,
certain language in making my point towards the end of each paragraph
rather than up front at the beginning (when introducing my point).”
Respondent 5
22

23. uts.edu.au
Challenge 3
Collaboration Analytics:
online and co-located teamwork
Teaching & Research partnerships being explored with:
DAB – CARLAB
Nursing – Simulation Lab
FASS – Collaborative & Creative Ideation
FEIT – Visualisation and Embodied Interaction
FEIT – Creativity and Cognition Studio
UTS Careers – Collaboration Assessment

24. Learning is no longer confined to UTS platforms
Analytics infrastructure to harvest and analyse activity on diverse sites
?
?
xAPI
OLT Project: Connected Learning Analytics Toolkit http://www.beyondlms.org

25. Social Network Visualisation of activity across multiple sites
25

26. COLLABORATION SPACES THAT TRACK F-F ACTIVITY
Analyse the students’ activity
traces for significant patterns
Timely feedback for personal and
team reflection

27. Multimodal data fusion and analysis… …to deliver visual analytics for reflection
e.g. this dashboard shows team member
participation on different modalities
Voice
Gesture
Pen
Touch
Applications for UTS researchers working on
high performance teams; group dynamics?
COLLABORATION SPACES THAT TRACK F-F ACTIVITY

28. THANK YOU! JOIN US IN CIC’S MISSION…
CIC catalyses the use of data and
analytics among UTS students,
educators, researchers and leaders
We teach human-centred data science •
design analytics tools for UTS • evaluate
these • disseminate internally and
globally
We aim to shape critical debate on big
data in education, and societal learning
utscic.edu.au