http://www.solaresearch.org/flare/solar-flare-uk/

1. Lightning
Presenta-ons!

2. Simon
Buckingham-­‐Shum

3. Visualizing
and
filtering
social
8es
in
SocialLearn
by
topic
and
type
Visualising
Social
Learning
in
the
SocialLearn
Environment.
Bieke
Schreurs
and
Maarten
de
Laat
(Open
University,
The
Netherlands),
Chris
Teplovs
(ProblemshiB
Inc.
and
University
of
Windsor),
Rebecca
Ferguson
and
Simon
Buckingham
Shum
(Open
University
UK),
SoLAR
Storm
webinar,
Open
University
UK.
hGp://bit.ly/LearningAnaly8csOU

4. Disposi8onal
Learning
Analy8cs
for
C21/LLL
Ques8oning
and
Different
social
challenging
network
paGerns
as
behaviours
as
proxies
for
Learning
proxies
for
CriKcal
RelaKonships
Curiosity
Cross-­‐contextual
Persevering
behaviours
as
proxies
behaviours
as
proxies
for
Meaning
Making
for
Resilience
Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn
http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium

5. Nicola
Avery

6. Medicines
&
Healthcare
Products
Regulatory
Agency
regulate :
applications :
Life
cycle
Pre-­‐clinical
Phase
1
Phase
2
Phase
3
MA
Approval
management
Clinical
Trials
Licence
&
Varia-ons
▼
900+ staff, agency internal systems, laptops/tablets, workload: 10-300 submissions
Learning & performance - procedures, assessment, consultation, committees,
timescales, data & document management / literacy

7. Learning analytics focus group
projects
Performance Support Data Assurance & Transparency Agency BI strategy
currently looking at feedback
- text analysis of existing feedback from training, develop examples
- ratings & recommendations for procedures – useful, accurate, up-to-date
evaluation report January 2013
“Well
done
you've
used
really
nice
language
in
that
email”
“you
seem
to
have
been
working
on
this
report
for
7
years”
“8
out
of
10
assessors
said
they
prefer…”

8. Doug
Clow

9.
• Data
Wrangling
– Demographics,
VLE
usage,
course
characteris8cs,
student
feedback
– Human
sense-­‐making
Doug
Clow

10. Joseph
Corneli

13. Adam
Cooper

14. Exponen8al
Random
Graph
Models
A
d
First
Experiments
with
Mutuality
a
m
Transi8vity
C
o
o
p
Homophily
er
(JI
S
C

15. e.g.
JISC
and
CETIS
Teams
• Showing
our
colours?
● Main
effect
● Homophily
● Mixing
edges
+
sender(base=c(-­‐4,-­‐21,-­‐29,-­‐31))
+
receiver(base=c(-­‐14,-­‐19,-­‐23,-­‐28))
+
nodematch("team",
diff=TRUE,
keep=c(1,3,4))
+
mutual
All
images
and
text
CC-­‐By:
Adam
Cooper,
2012

16. Martyn
Cooper

17. John
Doove

18. Exploring
Learning
AMore
paossibili8es
of
naly8cs
the
wareness
Enthusiasm!
Lessons
Learned
Vak
voor
Vak
User
Needs
UvAnaly-­‐
8cs
PinPoint
MAIS
ProF
Curri
Analy8cs
M
hGp://youtu.be/Xs3MsGPVivg
Seven
tangible
examples
to
refer
to
Community
of
various
Areas
of
work
to
be
experts
done…

19. Cath
Ellis

21. Unlikely
Very
unlikely
Neither
Likely
or
Unlikely
Very
unlikely
7%
2%
Unlikely
0%
2%
Neither
Likely
or
5%
Unlikely
11%
Before
Very
Likely
How
likely
AEer
32%
are
you
to
Likely
29%
use
this
Very
Likely
feedback?
64%
Likely
48%
Clearer
sense
of
where
they
sit
in
comparison
to
their
cohort
which
mo8vates
them
to
want
to
do
more
to
improve
Shining
aGen8on
to
important
areas
that
they
tend
to
neglect
Mo8va8ng
high
achieving
students
Seeing
a
bigger
picture
For
some
this
is
emo8onally
challenging
and
sensi8ve
but
for
others
it’s
not

22. Rebecca
Ferguson

23. Social
learning
analy-cs:
discourse
Challenge: Locate the exploratory dialogue
Manual analysis
identifies indicators
Category
Indicator
Challenge
But
if,
have
to
respond,
my
view
Cri8que
However,
I’m
not
sure,
maybe
Discussion
of
resources
Have
you
read,
more
links
Evalua8on
Good
example,
good
point
Explana8on
Means
that,
our
goals
Explicit
reasoning
Next
step,
relates
to,
that’s
why
Jus8fica8on
I
mean,
we
learned,
we
observed
Reflec8on
of
perspec8ves
of
others
Agree,
here
is
another,
take
your
point
23

24. Self-­‐training
framework
for
automa-c
exploratory
discourse
detec-on
• Framework
uses
cue
phrases
to
make
use
of
discourse
features
for
classifica8on
• Uses
a
k-­‐nearest
neighbours
instance
selec8on
approach
to
draw
on
topical
features

25. Dai
Griffiths

26. Mar8n
Hawksey

27. c MOOC Architecture
Blogs Daily Alert
(email/RSS)
LMS “
Central
store
filter
Black box
Social “ (aggregator)
Bookmarking
Twitter & Comments
Social media
Adapted from Siemens, 2012

28. c MOOC Analytics
Opportunity
• Open (ish) data
Issues
• Time limited
• "analytically cloaked"
• Darksocial
• Infrastructure/messy data

29. Jean
MuGon

30.
Engagement
AnalyKcs
Jean
MuGon,
Project
Manager
TwiGer
@myderbi
www.derby.ac.uk/ssis/JISC-­‐projects

32. Jonathan
San
Diego
• hGp://infiniterooms.co.uk/poster/

33. Mark
Stubbs

34. 1. Uniview
-­‐
Oracle-­‐based
data
warehouse
/
BI
repor8ng
since
2009
2. Used
R
randomForest
for
learning
tech
review
&
NSS
analysis
since
2010
3. Consistent
student
sa8sfac8on
data
collec8on,
10,770
respondents
2011
4. Star8ng
major
Analy8cs
project
(SQL
Server,
SSAS,
SSRS,
SP2010)
A
League
table
rankings
Marke)ng
&
Recruitment
Reputa)on
Processes
C
B
Learning,
Teaching,
Assessment
Student
Intake
Student
Reten)on
&
Personal
Development
(Aspira)ons,
A8tude
Success
&
Processes,
Facili)es
&
Abili)es)
Sa)sfac)on
&
Resources
Resource
alloca)on
All
Year
Numbers
A Recruit
to
target
B Improve
sa8sfac8on,
reten8on
&
success
C
Inform
decision-­‐makers
Prof
Mark
Stubbs
|
Head
of
Learning
&
Research
Tech
|
m.stubbs@mmu.ac.uk
|
twiGer.com/thestubbs

35. Annika
Wolff

36. students
Data
sources
VLE
TMA
Demographic
Other..
Who
is
struggling?
RETAIN
predic8ve
models
Why
are
they
Dashboard
visualisa8ons
struggling?

37. BUILDING
THE
PREDICTIVE
MODELS
Developed
and
tested
on
3
historic
data
sets
Compared:
decision
trees
and
SVM’s.
Compared:
VLE
only,
TMA
and
combined
MAIN
FINDINGS
• No
overall
clicking
measure
correlated
with
pass/fail:
focus
on
change
in
student
behaviour
instead
• High
precision
can
be
achieved
in
predic8ng
both
performance
drop
and
final
outcome
(pass/fail)
for
all
3
modules,
using
combined
VLE
and
TMA
data
• Demographic
data
can
improve
performance,
but
in
early
stages
the
VLE
ac8vity
is
the
most
informa8ve
data
source.
• Successfully
applied
2010
model
to
2011
data.
Even
some
success
across
modules.

38. Labs
www.triballabs.net
Learning
Analy8cs
R&D
Project
• Partnership
with
a
university
to
develop
a
Learning
Analy8cs
PoC:
– Predic8ve
model
which
can
predict
student
success
– Combine
data
from
mul8ple
administra8ve
and
ac8vity
sources
– Test
how
support
staff
can
interact
with
the
model
and
correctly
interpret
predic8ons
– Bring
together
visualisa8on
and
ac8on
–
onen
a
missing
element
@chrisaballard

39. Labs
www.triballabs.net
Mapping
Success
Factors
Academic
Integra-on
Engagement
Circumstances
Grades
VLE
Ac8vity
Social
Background
Library
Ac8vity
Proximity
Finance
Social
Integra-on
Prepara-on
for
HE
Forum
interac8on
Demographics
Qualifica8ons
@chrisaballard