Chancellor Nancy Zimpher's presentation at the SLN SOLsummit 2013 by SUNY Learning Network on Feb 27, 2013 0 views The SLN SOLsummit 2013 February 27, 2013 Syracuse, NY FACT2 Learning Analytics Task Group (LATG) Update http://mediasite.suny.edu/mediasite/Viewer/?peid=7eb3c62bb4b745cb8147c74f8062afae1d http://slnsolsummit2013.edublogs.org

1. FACT2
Learning
Analy0cs
Task
Group
(LATG)
Update
h>p://wiki.sln.suny.edu/display/FACT/Learning+Analy0cs+Task+Group

2. Learning
AData
BIG
naly0cs
Analy0cs

3. Let’s
hear
from
you….
• 0nyurl.com/factlatg13
LATG
Learning
Analy0cs

4. Q1:
What
do
you
know
about
learning
analy:cs?
1. A
lot
2. A
li>le
3. Unsure
4. What
is
learning
analy0cs?
LATG
Learning
Analy0cs

5. • “the
0mes
they
are
a-­‐
changing”
– Bob
Dylan
• “Technology
is
at
the
center
of…turbulence
in
our
0mes”
– Tony
Picciano,
CUNY
• “…collec0ng
traces
that
learners
leave
behind
and
using
those
traces
to
improve
learning”
– E.
Duval,
LAK
2012
Belgium
The
Rise
of
“Big
Data”

6. 1. Iden0fy
a
STRATEGY
and
course
of
ac0on
for
further
explora0on
and
implementa0on
of
Learning
Analy0cs
across
SUNY.
2. Provide
OPPORTUNITIES
for
SUNY
faculty
to
contribute
to
the
debate
and
best
prac0ces
on
Learning
Analy0cs.
3. Iden0fy
TOOLS
(so_ware)
and
PARTNERSHIPS
(business,
organiza0ons)
inside
and
outside
of
SUNY
and
recommend
how
best
to
leverage
these.
4. Iden0fy
and
share
best
PRACTICES
and
exemplary
uses
of
Learning
Analy0cs
across
SUNY.
5. Collaborate
with
campuses
to
iden0fy
exis0ng
POLICES
and
laws
(FERPA,
HIPAA,
etc.)
and
recommend
addi0onal
polices
as
needed
to
ensure
the
appropriate
and
ethical
use
of
Learning
Analy0cs
within
SUNY
LATG
Task
Group
Charge

7. • Learning
analy0cs
uses
so_ware
that
collects
and
analyzes
mul0ple
data
sets
related
to
the
process
of
learning
to
predict
and
impact
student
success.
• This
includes
data
collected
in
blended
and
online
learning
environments,
online
portals,
enrollment
data,
and
other
emergent
resources
connected
to
the
teaching
and
learning
experience.
• Learning
analy:cs
can
be
used
to…
– diagnose
student
needs,
– provide
feedback
to
the
student,
faculty,
instruc0onal
developer,
and
advisor,
– combine
with
data
from
other
learning
systems
to
generate
new
insights
about
learning
and
instruc0on.
Learning
Analy0cs
-­‐
Working
Defini0on

8. Examples
of
uses…
• Persistence
and
reten0on
(APUS)
• Intelligent/adap0ve
tutoring
(Carnegie
Mellon)
• Research
on
condi0ons
that
facilitate
learning
(CSU
Chico)
Learning
Analy0cs
&
Online
Learning

9. Q2:
Use
Learning
analy0cs
to
evaluate
student
achievement
of
program
and
course
level
learning
outcomes?
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

10. Q3:
Use
Learning
analy0cs
to
Iden:fy
academically
at-­‐risk
students
and
no:fy
students,
faculty
and/or
advisors.
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

11. Group
Discussion
WHAT
DO
YOU
THINK
ABOUT
LA
USES
?

12. Q4:
Use
Learning
analy0cs
to
Provide
automated
feedback
to
students
(ex:
quiz/test
feedback)
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

13. Q4:
Use
Learning
analy0cs
to
Provide
individualized
learning
paths
to
students
based
on
pre-­‐entry
condi:ons.
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

14. Q5:
Use
Learning
analy0cs
to
Provide
adap:ve
learning
paths
to
students
based
on
performance
in
course.
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

15. Group
Discussion
WHAT
DO
YOU
THINK
ABOUT
LA
USES
?

16. Q6:
Use
Learning
analy:cs
to
customize
course
delivery
to
student
learning
styles.
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

17. Q7:
Use
Learning
analy0cs
to
revise
course
content,
ac:vi:es,
assessments
and/or
course
structure.
1. In
widespread
use
2. In
limited
use
3. Not
in
use,
but
interested
4. Not
in
use,
not
interested
Your
input:
learning
outcomes

18. Group
Discussion
WHAT
DO
YOU
THINK
ABOUT
LA
USES
?

19. Where
is
the
data?
Stand-­‐alone
LMS
Plajorm
Plajorm
Analy0cs
Analy0cs
Discrete
Analy0cs
Tools
Iden0fying
tools
and
services

20. Picciano,
JALN
v16
no
3
Data
Flow
Examples

21.
SPSS
Iden0fying
tools
and
services

22. • Campus
LA
survey-­‐
in
process
– March
Webinar
to
highlight
uses
&current
thinking,
con0nue
dialogue…
– Policy
considera0on
discussion
– Evalua0on/demonstra0ons
from
vendors
• CIT
2013
presenta0on
(May
2013)
• Final
recommenda0ons
&
report
(May
2013)
LATG
Next
Steps

23. QUESTIONS?S?
Clare
Van
den
Bllink
cv36@cornell.edu
Greg
Ketcham
greg.ketcham@oswego.edud
h>p://wiki.sln.suny.edu/display/FACT/Learning+Analy0cs+Task+Group