This document outlines the Open University's strategy for developing and deploying learning analytics to improve student success. It discusses developing a vision and framework to guide the strategic use of analytics at both the institutional and individual student level. It also outlines plans to build institutional capabilities in data collection, analysis, and applying analytics to interventions. The goal is to apply analytics throughout a student's learning experience, from optimizing tutor assignments to monitoring student engagement and predicting at-risk students to help target support.

1. Designing Systemic Learning Analytics
at the Open University
Simon Buckingham Shum
Knowledge Media Institute
The Open University, UK
Strategy & Policy for Systemic Learning Analytics
SoLAR Open Course, 11th Oct 2013
https://learn.canvas.net/courses/182/wiki/designing-systemic-analytics-at-the-open-university
Belinda Tynan
Pro-Vice-Chancellor Learning & Teaching
The Open University, UK

2. overview
Belinda: developing an institutional strategy
— framework and implementation
Simon: research perspectives
— 3 metaphors for systemic analytics
Discussion

3. Learning and Teaching
Strategy for Systemic Deployment of
Analytics at the Open University
Belinda Tynan, Pro-Vice-Chancellor, Learning and Teaching
Kevin Mayles, Senior Manager, Learning and Teaching

4. Learning and Teaching
Analytics for student success vision
p.4
A clear vision has been developed to galvanise effort across the institution
on the focused use of analytics to drive student success
Vision
To
use
and
apply
informa2on
strategically
(through
specified
indicators)
to
retain
students
and
progress
them
to
complete
their
study
goals
Mission
This
needs
to
be
achieved
at
:
• a
macro
level
to
aggregate
informa5on
about
the
student
learning
experience
at
an
ins5tu5onal
level
to
inform
strategic
priori5es
that
will
improve
student
reten5on
and
progression
• a
micro
level
to
use
analy5cs
to
drive
short,
medium
and
long-­‐term
interven5ons

5. Learning and Teaching
What drives student success?
p.5
We have an emerging picture of the factors affecting student success
based on existing statistical analyses, literature and “institutional
knowledge” and our current use of associated indicators
Framework adapted from Woodley et. al. (2001) Student Progress in Distance Education:
Kember’s model re-visited
• Early
contact
• Early
engagement
• Study
calendar
/
scheduling
• Tutor
support
• Peer
support
&
belonging
• Study
habits
• Employer
support
• Family
support
• Personal
life
events
• Financial
issues
• Advice
on
course
choice
• Subject
studied
• Prepara5on
for
study
• Learning
design
• Quality
of
study
materials
• Workload
• Module
assessment
strategy
• Language
ability
• Previous
educa5on
/
OU
study
• Ethnic
group
• Socio-­‐economic
background
• Disability
• Age
• Study
goal
/
mo5va5on
• Gender
Entry
characteris-cs
Academic
compa-bility
Social
and
academic
integra-on
External
factors
Student
Success
Indicators
used
in
exis-ng
analy-cs
There
are
a
number
of
indicators
with
suppor5ng
evidence
that
we
currently
use
in
our
analysis
models
Clear
evidence
of
impact
but
currently
not
used
in
analy-cs
We
have
a
number
of
factors
for
which
there
is
clear
evidence
of
the
impact
on
success
but
are
not
being
used
in
current
analy5cs
models
due
to
lack
of
data
or
insufficient
inves5ga5on
Unclear
evidence
base
There
are
a
number
of
factors
that
the
OU
believes
or
literature
suggests
have
an
impact
on
student
success
but
where
we
have
no
clear
evidence
at
this
5me
due
either
to
lack
of
data
availability
or
insufficient
inves5ga5on
Results
from
a
review
of
exis-ng
evidence
on
the
drivers
of
student
success
are
giving
us
a
mixed
picture
Indicators
with
evidence
of
no
impact
There
are
a
number
of
indicators
with
suppor5ng
evidence
that
suggest
they
have
a
minimal
impact
on
success

6. Learning and Teaching
Developing institutional capabilities and strengths
3 year strategic roadmap
The OU is developing its capabilities in 10 key areas that build the
underpinning strengths required for the effective deployment of analytics
We
need
to
ensure
we
have
the
right
architecture
and
processes
for
collec5ng
the
right
data
and
making
it
accessible
for
analy5cs
–
we
need
a
‘big
data’
mind-­‐set
The
university
needs
world
class
capability
in
data
science
to
con5nually
mine
the
data
and
build
rapid
prototypes
of
simple
tools,
and
a
clear
pipeline
for
the
outputs
to
be
mainstreamed
into
opera5ons
Benefits
will
be
realised
through
exis5ng
business
processes
impac5ng
on
students
directly
and
through
enhancement
of
the
student
learning
experience
–
we
will
develop
an
‘analy5cs
mind-­‐set’
in
these
areas
p.6

7. Learning and Teaching
Framework for harnessing analytics for student
success through driving interventions
p.7
Analytics will be applied throughout the cycle of the student learning
experience
Analy-cs
applied…
Example
business
processes
Example
datasets
used
For
ac-on
Op5mise
student
alloca5on
to
tutor
groups
Development
of
learning
systems
Assessment
strategy
and
scheduling
Student
pass/fail
predic5ons
Study
behaviour
profiles
Pass
rates
modelling
In
ac-on
Early
contact
with
‘at
risk’
students
Module
presenta5on
issue
flagging
Student
‘at
risk’
predic5ve
indicators
Helpdesk
contact
records
On
ac-on
Annual
module
and
programme
review
Learning
design
Module
performance
KPIs
Learning
design
profiles
“In Action, On Action” from Donald Schön The Reflective Practitioner

8. Learning and Teaching
Applying ‘in action’ analytics to drive student success?
p.8
We will use analytics to put key information relating to student success in the hands
of those in a position to take action
Tutor
Group
List
Students’
study
history
Feedback
from
previous
tutors
Predicted
probability
of
passing
‘At
risk’
factors
Associate
Lecturer
Plan
early
contact
with
most
at
risk
students
Monitor
engagement
prior
to
first
TMA
Refer
issues
to
SST
quickly
Weekly
Alert
Dashboard
Weekly
update
of
students’
predicted
probability
of
passing
/
progressing
List
of
most
‘at
risk’
students
this
week
Target
resources
at
most
at
risk
students
Call
or
email
students
on
at
risk
list
to
offer
support
No5fy
ALs
of
any
issues
arising
in
their
groups
Student
Support
Team
Module
performance
report
Predicted
pass
rate
vs
target
pass
rate
updated
during
presenta5on
Analysis
of
online
learning
ac5vity
usage
/
engagement
pa^erns
Iden5fy
any
issues
with
the
module
whilst
in
presenta5on
and
take
ac5on
to
rec5fy
Evaluate
the
use
of
learning
assets
to
inform
future
produc5on
Faculty
Academics
Senior
Management
Student
success
measures
Indicators
derived
from
sta5s5cal
modelling
that
underpin
student
progression
measures
Monitor
student
progression
forecasts
against
target
–
iden5fy
correc5ve
ac5on
Target
resources
at
specific
‘pinch
points’
in
the
student
journey

9. Learning and Teaching
Evaluating impact and driving action
p.9
The basis for evaluation needs to link interventions to measurable
outcomes of student success

10. Learning and Teaching
Evaluating impact and driving action
p.10
The basis for evaluation needs to link interventions to measurable
outcomes of student success
STUDENT
SUCCESS

11. Learning and Teaching
Evaluating impact and driving action
p.11
The basis for evaluation needs to link interventions to measurable
outcomes of student success
STUDENT
SUCCESS
Interven-ons
For
ac5on
In
ac5on
On
ac5on

12. Learning and Teaching
Evaluating impact and driving action
p.12
The basis for evaluation needs to link interventions to measurable
outcomes of student success
Governance
and
Management
STUDENT
SUCCESS
Interven-ons
For
ac5on
In
ac5on
On
ac5on

13. Learning and Teaching
Evaluating impact and driving action
p.13
The basis for evaluation needs to link interventions to measurable
outcomes of student success
Drivers
of
student
success
Governance
and
Management
STUDENT
SUCCESS
Interven-ons
For
ac5on
In
ac5on
On
ac5on

14. Learning and Teaching
Improve
ins-tu-onal
capabili-es
and
processes
Evaluating impact and driving action
p.14
The basis for evaluation needs to link interventions to measurable
outcomes of student success
Drivers
of
student
success
Governance
and
Management
STUDENT
SUCCESS
Interven-ons
For
ac5on
In
ac5on
On
ac5on

15. Learning and Teaching
Improve
ins-tu-onal
capabili-es
and
processes
Evaluating impact and driving action
p.15
The basis for evaluation needs to link interventions to measurable
outcomes of student success
Drivers
of
student
success
Governance
and
Management
STUDENT
SUCCESS
Interven-ons
For
ac5on
In
ac5on
On
ac5on
Are
we
seeing
expected
outcomes
of
our
interven5ons?
Are
we
doing
the
right
things
as
guided
by
the
evidence?

16. Analytics will drive action to increase student success
Vision: To use and apply information strategically (through specified indicators) to
retain students and progress them to complete their study goals

17. Analytics will drive action to increase student success
Vision: To use and apply information strategically (through specified indicators) to
retain students and progress them to complete their study goals
Recruit Retain Progress Complete
Success
outcomes
and
leading
indicators

18. Analytics will drive action to increase student success
Vision: To use and apply information strategically (through specified indicators) to
retain students and progress them to complete their study goals
Recruit Retain Progress Complete
Success
outcomes
and
leading
indicators
Student
support
ac5vi5es
Learning
&
teaching
ac5vi5es
Measures
of
our
opera5onal
performance
and
interven5ons
Drivers
of
student
success
Evidence
of
the
drivers
of
student
success
guides
what
we
do
and
what
we
measure

19. Analytics will drive action to increase student success
Vision: To use and apply information strategically (through specified indicators) to
retain students and progress them to complete their study goals
Recruit Retain Progress Complete
Success
outcomes
and
leading
indicators
Student
support
ac5vi5es
Learning
&
teaching
ac5vi5es
Measures
of
our
opera5onal
performance
and
interven5ons
Dashboards
/
Reports
/
Tools
Ins5tu5onal
Dashboard
PVCs
Deans
Programme
Directors
Module
Teams
Student
Support
Teams
Indicators
and
measures
fed into
dashboards
and reports
at relevant
levels
Drivers
of
student
success
Evidence
of
the
drivers
of
student
success
guides
what
we
do
and
what
we
measure

20. Analytics will drive action to increase student success
Vision: To use and apply information strategically (through specified indicators) to
retain students and progress them to complete their study goals
Recruit Retain Progress Complete
Success
outcomes
and
leading
indicators
Student
support
ac5vi5es
Learning
&
teaching
ac5vi5es
Measures
of
our
opera5onal
performance
and
interven5ons
Dashboards
/
Reports
/
Tools
Ins5tu5onal
Dashboard
PVCs
Deans
Programme
Directors
Module
Teams
Student
Support
Teams
Indicators
and
measures
fed into
dashboards
and reports
at relevant
levels
Drivers
of
student
success
Evidence
of
the
drivers
of
student
success
guides
what
we
do
and
what
we
measure
ACTION
Interven-on

21. Analytics will drive action to increase student success
Vision: To use and apply information strategically (through specified indicators) to
retain students and progress them to complete their study goals
Recruit Retain Progress Complete
Success
outcomes
and
leading
indicators
Student
support
ac5vi5es
Learning
&
teaching
ac5vi5es
Measures
of
our
opera5onal
performance
and
interven5ons
Dashboards
/
Reports
/
Tools
Ins5tu5onal
Dashboard
PVCs
Deans
Programme
Directors
Module
Teams
Student
Support
Teams
Indicators
and
measures
fed into
dashboards
and reports
at relevant
levels
Drivers
of
student
success
Evidence
of
the
drivers
of
student
success
guides
what
we
do
and
what
we
measure
ACTION
Interven-on
Evalua-on
of
the
outcomes
from
interven5ons
increases
our
evidence
base
of
what
drives
student
success

22. questions/comments?

23. 3 metaphors for
systemic analytics
1. the aquarium
2. from exoskeleton to nervous system
3. resilience through biodiversity

24. metaphor 1
the aquarium
systems strategy: research the key variables for
a healthy ecosystem and evolve predictive
models as rapidly as possible to detect variance

25. Aquarium science enables aquarium analytics
which monitor the health of the ecosystem

26. fish
aquarium science
learners?
learning science
instructional design
It’s all about knowing what to watch

27. Purdue University Signals: exemplar
‘healthy ecosystem’ variables
27

28. Purdue University Signals: exemplar
‘healthy ecosystem’ variables
28
Key variables identified:
• ACT or SAT score
• Overall grade-point average
• CMS usage composite
• CMS assessment composite
• CMS assignment composite
• CMS calendar composite

29. Hmmm…
no learning sciences
no learning design
underpinning these predictive models of student success
models based on a mix of
institutional know-how
about student success, and analysing
behavioural data
29

30. the opportunity for the
learning sciences
to combine with your university’s
collective intelligence
30

31. Predictive modelling of student outcomes
Registra-on
PaMern
CRM
contact
VLE
interac-on
Assignment
grades
Demo-­‐
graphics
?
Can we combine datasets,
and use machine learning to
build models to identify
‘signature’ patterns associated
with different kinds of
students?
Library
interac-on
OpenLearn
interac-on
FutureLearn
interac-on
App
X
interac-on
OU
track
record

32. Predictive modelling of student outcomes
Registra-on
PaMern
CRM
contact
VLE
interac-on
Assignment
grades
Demo-­‐
graphics
?
Does VLE data carry
information that provides more
precise early identification of
failing students than is
currently possible?
Simple example (just 3 demographic attributes and VLE):
Input:
Demographic data: New/Continuing student,
Sex, Previous education
VLE interactions without qualifying the type
(any click counts)
Goal:
Evaluate the probability that the student
does not submit TMA1 or submits and
scores lower than 40.
Method: Naïve Bayes network (e.g. see Bishop,
2009)
Library
interac-on
OpenLearn
interac-on
FutureLearn
interac-on
App
X
interac-on
OU
track
record

33. Learning and Teaching
Back to the OU’s analytics framework

34. Learning and Teaching
Predictive modelling within the framework
VLE
user
trace
data
/
student
demographics
/
academic
achievement
Strategic internal funding to advance a promising technique from an externally
funded (JISC) project, and embed within OU student support processes:
A.L. Wolff and Z. Zdrahal (2012). Improving Retention by Identifying and Supporting “At-risk” Students. EDUCAUSE Review
Online, July-August 2012. http://www.educause.edu/ero/article/improving-retention-identifying-and-supporting-risk-students
Zdenek Zdrahal
Lead, KMi Predictive Modelling Team
http://kmi.open.ac.uk/people/member/zdenek-zdrahal

35. Learning and Teaching
Predictive modelling within the framework
Develop
and
Validate
Predic-ve
Models
of
student
success
(module
comple-on)
in
order
to
trigger
more
-mely
alerts
VLE
user
trace
data
/
student
demographics
/
academic
achievement

36. Learning and Teaching
Predictive modelling within the framework
Develop
and
Validate
Predic-ve
Models
of
student
success
(module
comple-on)
in
order
to
trigger
more
-mely
alerts
Requirements
to
mainstream
the
models
in
the
VLE
VLE
user
trace
data
/
student
demographics
/
academic
achievement

37. Learning and Teaching
Predictive modelling within the framework
Develop
and
Validate
Predic-ve
Models
of
student
success
(module
comple-on)
in
order
to
trigger
more
-mely
alerts
Prototype
Student
Support
Team
dashboards
Requirements
to
mainstream
the
models
in
the
VLE
VLE
user
trace
data
/
student
demographics
/
academic
achievement

38. Learning and Teaching
Predictive modelling within the framework
Develop
and
Validate
Predic-ve
Models
of
student
success
(module
comple-on)
in
order
to
trigger
more
-mely
alerts
New
tool
for
Student
Support
Teams,
to
enable
-mely
interven-on.
E.g.
real-­‐-me
traffic
lights
on
at
risk
students
Requirements
to
mainstream
the
models
in
the
VLE
VLE
user
trace
data
/
student
demographics
/
academic
achievement
Prototype
Student
Support
Team
dashboards

39. Learning and Teaching
Predictive modelling within the framework
Develop
and
Validate
Predic-ve
Models
of
student
success
(module
comple-on)
in
order
to
trigger
more
-mely
alerts
New
tool
for
Student
Support
Teams,
to
enable
-mely
interven-on.
E.g.
real-­‐-me
traffic
lights
on
at
risk
students
Modules
are
accompanied
by
machine-­‐readable
metadata
that
increases
the
power
of
machine
learning
when
it
comes
to
data
analysis
Requirements
to
mainstream
the
models
in
the
VLE
VLE
user
trace
data
/
student
demographics
/
academic
achievement
Prototype
Student
Support
Team
dashboards

40. Problem specification
Learning to dynamically id an at-risk student
We are hereWe know

41. Problem specification
Learning to dynamically id an at-risk student
We are hereWe know We predict

42. e.g. The Retain project
Does VLE activity add value to predictive models?
We know We predict
Sex
Educ
New/
Cont
VLE
Model the probability of failing at
TMA1 which is known to be a key
predictor of final outcome
either by not submitting TMA1,
or by submitting with score < 40.
TMA1

43. • Demographic
profile
1
– X
– Y
– Z
Sex
Educa5on
N/C
TMA1
Without
VLE
data:
Probability
of
failing
at
TMA1
=
18.5%
Student profile 1

44. Student profile 1
• Demographic
profile
1
– X
– Y
– Z
Sex
Educa5on
N/C
TMA1
Without
VLE
data:
Probability
of
failing
at
TMA1
=
18.5%
Sex
Educa5on
N/C
VLE
TMA1
Clicks
Probability
Nr
of
students
0
64%
4
1-­‐20
44%
3
21-­‐100
26%
5
101-­‐800
6.3%
14
With
VLE
data,
a
higher
fidelity
story:

45. Student profile 2
Sex
Educa5on
N/C
TMA1
Without
VLE
data:
Probability
of
failing
at
TMA1
=
7.7%
• Demographic
profile
2
– X
– Y
– Z

46. Student profile 2
Sex
Educa5on
N/C
TMA1
Without
VLE
data:
Probability
of
failing
at
TMA1
=
7.7%
Sex
Educa5on
N/C
VLE
TMA1
Clicks
Probability
Nr
of
students
0
39%
35
1-­‐20
22%
74
21-­‐100
11.2%
178
101-­‐800
2.4%
461
With
VLE
data,
a
higher
fidelity
story:
• Demographic
profile
2
– X
– Y
– Z

47. Potential to augment student support teams
with predictive modelling once validated
Query
VLE
interac-on
Assignment
grades
Demo-­‐
graphics
OU
track
record

48. Potential to augment student support teams
with predictive modelling once validated
7 of your students have fail trajectory
BUT prioritize Nigel, then Sue, then Ian
because
- has not engaged with VLE
- at least one TMA below 40
- has not submitted 5 TMAs
Query
VLE
interac-on
Assignment
grades
Demo-­‐
graphics
OU
track
record

49. Why do I need a variable ML approach?
Can’t I just use one method (off the shelf)?
Registra5on
Pa^ern
CRM
interac5ons
Library
interac5on
FutureLearn
interac5on
Train
and
Learn
as
new
data
is
added
using
variable
methods
Methods successfully tested, to be further developed:
• Induction of decision tree (ID3, C4.5 from the Weka toolkit)
• Support Vector Machine (from Weka)
• Bayes network (Microsoft Infer.NET; SamIam - Stanford Univ.)
• Naïve Bayes (see the example and Demo Cases)
• Linear regression
• Logistic regression
• GUHA (General Unary Hypotheses Automaton)

50. metaphor 2
from exoskeleton
to nervous system
systems strategy: embed faster feedback loops,
and build sensemaking capacity at all levels

51. Evolving the OU from a digital
exoskeleton to a nervous system?
Ed Dumbill: http://strata.oreilly.com/2012/08/digital-nervous-system-big-data.html

52. Learning and Teaching
The OU’s collective intelligence
Macro Level Analytics
Towards multilevel systemic analytics for student success
Designing better feedback loops at all levels of learning (students + staff)
Micro Level
Analytics
Student Interaction Traces

53. Learning and Teaching
The OU’s collective intelligence
Macro Level Analytics
Towards multilevel systemic analytics for student success
Designing better feedback loops at all levels of learning (students + staff)
Micro Level
Analytics
Student Interaction Traces
Student Support Teams
Associate Lecturers
Researchers
interpretation/intervention

54. Learning and Teaching
The OU’s collective intelligence
Macro Level Analytics
Towards multilevel systemic analytics for student success
Designing better feedback loops at all levels of learning (students + staff)
Micro Level
Analytics
Student Interaction Traces
Student Support Teams
Associate Lecturers
Researchers
interpretation/intervention
Data Wranglers
Researchers

55. Learning and Teaching
The OU’s collective intelligence
Macro Level Analytics
Towards multilevel systemic analytics for student success
Designing better feedback loops at all levels of learning (students + staff)
Micro Level
Analytics
Student Interaction Traces
VC Executive
Faculties
Module Teams
Student Support Teams
Associate Lecturers
Researchers
interpretation/intervention
Data Wranglers
Researchers

56. Learning and Teaching
The OU’s collective intelligence
Macro Level Analytics
Towards multilevel systemic analytics for student success
Designing better feedback loops at all levels of learning (students + staff)
Micro Level
Analytics
Student Interaction Traces
VC Executive
Faculties
Module Teams
Student Support Teams
Associate Lecturers
Researchers
interpretation/intervention
Data Wranglers
Researchers
Visual Analytics Design
Quality Data, Integrated
Analytics Competency Team
Organisational Capacity building
Analytics Research
Computational Platforms

57. metaphor 3
build resilience
systems strategy: MOOCs can be viewed as a system-
level ‘shock’ to the HigherEd ecology (‘regime shift’?)
build resilience by expanding our diversity and capacity
to sense the dynamic environment

58. MOOCs are an innovation and research
platform — analytics will be critical
http://www.ted.com/talks/daphne_koller_what_we_re_learning_from_online_education.html
http://people.kmi.open.ac.uk/sbs/2013/01/emerging-mooc-data-analytics-ecosystem
http://www.slideshare.net/abelardo_pardo/pushing-the-mooc-envelope-with-learning-analytics
http://www.moocresearch.com/research-initiative/about#Cost,%20Performance%20Metrics%20and%20Learner%20Analytics

59. FLx: experimental analytics ecosystem
Partners share ideas, workflows, analytics, and visualizations, collaborating
around common interests.

60. FLx: experimental analytics ecosystem
Partners share ideas, workflows, analytics, and visualizations, collaborating
around common interests.

61. FLx: experimental analytics ecosystem
Partners share ideas, workflows, analytics, and visualizations, collaborating
around common interests.

62. FLx: experimental analytics ecosystem
Partners share ideas, workflows, analytics, and visualizations, collaborating
around common interests.

63. FLx: experimental analytics ecosystem
Partners share ideas, workflows, analytics, and visualizations, collaborating
around common interests.

64. FLx: experimental analytics ecosystem
Partners share ideas, workflows, analytics, and visualizations, collaborating
around common interests.

65. Workflow for social network analytics in NodeXL:
are learners forming effective peer-relationships?
Import data into OpenRefine
Reshape using template
Export data to CSV
Process in NodeXL
and generate network
Martin Hawksey
http://mashe.hawksey.info/2013/02/lak13-recipes-in-capturing-and-analyzing-data-using-sna-on-canvas-discussions-with-nodexl-for-when-its-not-a-snapp

66. Workflow for quantifying reflections in forum
posts: what elements of reflection are evident?
Convert discussion threads in comma-separated file format
Annotate text segments using
custom components for UIMA
Convert results in CSV
Ullmann, T. D., Wild, F., & Scott, P. (2012). Comparing Automatically Detected Reflective Texts with Human Judgements. In 2nd Workshop on Awareness and
Reflection in Technology-Enhanced Learning. Presented at the 7th European Conference on Technology-Enhanced Learning, Saarbruecken, Germany.
Retrieved from http://ceur-ws.org/Vol-931/paper8.pdf
Inspect and analyse data with R
Reason over annotations with Drools

67. Workflow for academic writing analytics: to
what extent does student writing display the
hallmarks of scholarly argument?
Extract submitted essay drafts from Course XYZ
Convert to text files for XIP
Analyse using rhetorical parser
Render in custom dashboard
Annotate onto source text
Simsek D, Buckingham Shum S, Sándor Á, De Liddo A and Ferguson R. (2013) XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of
Scientific Metadiscourse. 1st International Workshop on Discourse-Centric Learning Analytics, at 3rd International Conference on Learning Analytics &
Knowledge. Leuven, BE (Apr. 8-12, 2013). Open Access Eprint: http://oro.open.ac.uk/37391

68. Thank you… Q&A
Belinda Tynan
http://www.open.ac.uk/about/main/admin-and-governance/executive-team/pro-vice-chancellor-learning-and-teaching
Simon Buckingham Shum
http://simon.buckinghamshum.net / @sbskmi