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

1. Breakout
groups
feedback

2. Reten%on
and
success
• Reten%on
and
success
are
dis%nct,
but
linked.
Qualita%ve
vs
binary.
• Applica%ons:
quick/early
drop-­‐out,
adapa%ve
learning.
• Ethical
issues.
• Media%ng
feedback,
using
analy%cs
to
present
the
model
with
the
ra%onale,
used
as
the
basis
for
a
personalised
conversa%on.
Photo
(CC)
Trey
Ratcliff
hJp://www.flickr.com/photos/stuckincustoms/4622806283/

3. Mul%ple
Purposes
• Aggrega%on
Ethics
• Interven%on
• Emo%ons
• Mo%va%on
• Informed
decision
making
• Anxiety
• ‘De-­‐ • Surveillance
modularisa%on’
(holis%c
• Privacy
informa%on)
• Ipsa%ve
vs
norm
• Transparency
informa%on
Opera%onalisa%on
Mul%ple
audiences
• Selec%ng
data
sets
• Different
purposes
• Timeliness
and
efficacy
• Same
data
sets
• evalua%on
• Granularity
• Interpreta%on
and
• Interac%vity
clarity
• Proprietary
tool
providers
• Training
and
sense
making
Dashboards
preemp%ng
our
needs/
wants
• Pedagogically
drivers

4. Dashboard
Examples
Student
• How
am
I
doing
compared
to
cohort?
Tutor
• Is
what
I’m
doing
with
my
students
working?
Ins%tu%on
• Which
students
are
most
likely
to
drop
out?
PSRB
• Are
any
students
gradua%ng
from
this
ins%tu%on
without
all
of
the
required
learning
outcomes?
Researchers
• Across
the
sector
which
ins%tu%ons
produce
the
best
graduates
in
each
discipline?

5. Analy5cs
for
Student
Success
&
Reten5on:
Issues
Pre-­‐fail
Dangers
of
a
Pre-­‐Crime
Unit
Ethics
of
interven5on:
Just
for
those
who
are
failing?
What
about
the
rest?
Beware
self-­‐fulfilling
failure
prophecies!
“Dear
<field1>…”
Beware
back-­‐firing
personalisa%on
expecta%ons:
“So
I
really
am
just
a
number”
Informed
interven%ons
hopefully
changing
learners’
futures
for
the
beJer…
But
what
does
that
do
for
datasets
and
historical
comparison?
Important
to
collect
data
about
interven%ons
to
assess
their
impact
amongst
other
variables
Beware:
can’t
count,
doesn’t
count:
we’re
in
a
complex
people
business!

6. Pedagogy
&
LA

7. Issues
• How
do
we
measure
learning
(rather
than
‘success’
in
assessments)
• Approximate
proxies
for
learning…
• Shouldn’t
assessment
be
our
‘best
measure’
of
learning
–
well,
perhaps
it
should
be
a
suite
of
analy%cs
• What
‘knowledge’
do
we
want
from
our
graduates
• ‘Recipe’
issue
of
LA?
–
so
we
have
to
make
sure
we’re
looking
for
the
‘right’
processes
• Assessment/analy%cs:
Snapshots,
con%nuity,
and
change
metrics;
how
can
they
be
used?
• Analy%cs
driven
by
what
we
want
to
achieve
rather
than
what
data
is
available

8. Examples
• Dialogue
analysis,
perhaps
analysis
of
use
of
social
networks
• LA
as
pedagogy
v
LA
for
pedagogy
–
LA
which
feeds
back
in
to
‘improving’/adap%ng.
LA
can
help
us
challenge
our
assump%ons
about
how
the
learning
is
taking
place.
Can
LA
allow
us
to
hypothesis
test
our
(as
teachers)
assump%ons
about
learning?
• Pass
rate
and
online
ac%vity
has
a
correla%on
–
effec%ve
‘proxy’?

9. Data
sources

10. Issues
• Availability
• Awareness
of
data
• Quality
collec%on
• Enrich
(combining
data)
• Sharing
(ethics,
• Private
commercially
sensi%ve)
• Infrastructure
• Paying
to
access
your
own
data
• Planning
in
rapidly
evolving
• Need?
area
(itera%ons)
• Granularity
(nano)
• Data
ownership
• Not
everything
is
online
–
• Purpose
no
footprint
(overall
• Culture
change
visibility
of
interac%ons)
• Volume

11. Examples
• TINCAN
API
• IBM
–
(data
don’t
ask,
don’t
get)
• midata