Crowds can support learning and knowledge creation. A framework using institutions and algorithms can help assure good outcomes - Wikipedia, Edx.org and Giffgaff are used to explain the framework. Presentation for KM 2012 in Sao Paulo, Brazil.

1.
Knowledge
from
Crowds
–
Be1er
with
Ins6tu6ons
+
Algorithms
h1p://goo.gl/q1DNL
Shaun
Abrahamson
@shaunabe

2. Tap
the
crowd
for
learning

3. Where
did
Stanley
come
from?

4. Crowd
data
recycled
into
knowledge

5. Collec6ve
contribu6ons
into
holis6c
understanding

6. Compe6ng
for
be1er
predic6ons

7. Our
signals
into
rankings

8.
New
type
of
ins6tu6on
to
deliver
mobile
services

9. Influence
Capital
Assets
Labor
Data
The
crowd
as
the
gateway
to
critical
resources

10. Open Innovation, Co-Creation, Micro Tasks
Sharing, Commenting, Reviewing
“Big Data”
Influence
Capital
Assets
Labor*
Data
*
very
often
“knowledge”
work

11. Labor
/
Knowledge
Work
Influence
Capital
Assets
Data
Institutions
vs
Algorithms

12. 4 PERSPECTIVES!
!
OUTCOMES!
PEOPLE!
TOOLS!
ORGANIZATION!

13. OUTCOMES!

14. Ronald
Coase
“Given
that
produc6on
could
be
carried
on
without
any
organiza6on
that
is,
firms
at
all,
why
and
under
what
condi6ons
should
we
expect
firms
to
emerge?”
About
75
years
ago
Why
do
we
organize
work
in
a
certain
way?

15. !
R + D! Production! Operations! Marketing! Sales!
!
New
Ins6tu6ons
+
tools

16. !
R + D! Production! Operations! Marketing! Sales!
!
New
data
+
algorithms

17. CASE:!
What went wrong at Wikipedia?!

18. Source: Mary Meeker presentation at All things D. !
The
end
of
all
other
encyclopedia
business
models

19. Retention after 1 year (%)
Active Editors
Why
aren’t
editors
staying?

20. PEOPLE!
!
FINDING AND MOTIVATING
THE MOST IMPORTANT
KNOWLEDGE RESOURCES!

21. 175 million people on !
What
skills
might
you
tap
into?

22. Stuff! Money!
Attention! Experience!
Good!
Why
will
they
par6cipate?

23.
What
data
by-­‐products
might
you
rely
on?

24.
Why
did
you
start
contribu6ng
to
Wikipedia?

25.
What
kind
of
work
environment
do
you
want
?

26. ORGANIZATION!
!
WHAT INSTITUTITIONS
ARE CRITICAL TO BENEFIT
FROM CROWD LABOR +
INFLUENCE!

27. Elinor
Ostrom
“It's
a
problem,
it's
just
not
necessarily
a
tragedy
...
The
problem
is
that
people
can
overuse
[a
shared
resource],
it
can
be
destroyed,
and
it
is
a
big
challenge
to
figure
out
how
to
avoid
that.”
About
2
years
ago
Organizing
to
resolve
the
“Tragedy
of
Commons”

28.
Who
owns
what?
Brand
vs
IP
vs
Confiden6ality

29.
Cheap
access
to
dispute
resolu6on

30. Collec6ve
choice
processes

31. Sanc6on
bad
behavior
-­‐
Don’t
feed
the
trolls

32.
Nes6ng
to
scale

33. The
community's
role,
as
some
kind
of
nebulous
science-­‐fic6on
super-­‐en6ty,
is
to:
+
Organize
and
edit
individual
pages
+
Structure
naviga6on
between
pages
+
Resolve
conflict
between
individual
members
+
Re-­‐engineer
itself
-­‐-­‐
crea6ng
rules
and
pa1erns
of
behavior
There
are
other
roles
J
Source
-­‐
h1p://meta.wikimedia.org/wiki/The_Wikipedia_Community
How
Wikipedia
community
sees
itself

34. TOOLS!
!
!
COLLECTING DATA AND
CREATING NEW
UNDERSTANDING!

35.
Making
it
easier
to
contribute

36. My Klout!
My Giving (Crowdtwist)!
My Creative Impact(Jovoto)!
Understanding
individual
contribu6ons

37.
Understanding
collec6ve
health
and
performance

38.
Rocket
Science
vs
People
Science?

39. Making
sense
of
all
that
data

40.
Wikimedia
founda6on’s
focus
People
+
Tools

41. CASE: EDX.ORG!
!
NEW EDUCATIONAL
INSTITUTIONS !
+ !
DATA TO GET SMARTER
ABOUT EDUCATION!

42.
Content
+
Community
=
Learning

43. 155,000 registered!
23,000 tried the first problem set!
9,000 passed the midterm!
7,157 passed the course!
Represents
about
40
years
worth
of
classes
at
MIT

44.
Ricardo
+
Arthur
doing
“online
learning”

45. “One of the best things about 6.002x was the
community built by the students themselves.
The atmosphere was great: people shared
their enthusiasm and knowledge, and lended
a hand to those like me who didn’t have the
basics for the course.”
- Arthur Amaral, 18 years old, Brazil
Source: http://blog.edx.org/
Community
not
just
content

46.
Ins6tu6ons
-­‐
Nes6ng
+
Collec6ve
Choice

47. Anant
Agarwal
“We
can
watch
how
many
a1empts
students
made
before
they
got
an
exercise
right,
and
if
they
got
it
wrong,
what
they
used
to
try
to
find
a
solu6on.
Did
they
go
to
the
textbook,
go
back
and
watch
the
video,
go
to
the
forum
and
post
a
ques6on?”
About
1
month
ago
Data
to
learn
how
to
teach

48. CASE: GIFFGAFF
[TELEFONICA]!
!
NEW INSTITUTIONS!
TO BENEFIT FROM THE
KNOWLEDGE OF !
CUSTOMERS!

49.
Social
produc6on
for
a
complex
service

50.
Homepage
hints
at
how
this
works

51.
What
tasks
can
be
performed?

52. Value Created!
!
R + D! Production! Operations! Marketing! Sales!
!
Income/Expenses!
Who
is
doing
what
on
GiffGaff

53. From
sales
+
support
to
new
app
development

54. Encouraging
Par6cipa6on
+
Rewarding
Behavior

55.
Gaming
the
system
-­‐
posts
from
users
who
you
suspect
are
abusing
the
payback
system
by
using
mul6ple
accounts
to
give
themselves
solu6ons
or
kudos.
Tou0ng
for
SIMS/Kudos
-­‐
posts
which
are
ac6vely
asking
for
kudos
or
solu6ons,
it
is
fine
to
have
this
in
your
signature
but
not
to
ask
in
a
post/topic.
Incorrect
Accepted
Solu0ons
-­‐
if
you
spot
an
accepted
solu6on
which
is
incorrect
or
if
a
user
has
accepted
one
of
their
own
responses
as
a
solu6on
unjus6fiably.
Incorrect
Tags
-­‐
If
you
see
that
a
post
has
been
tagged
with
an
irrelevant
or
inappropriate
tag.
Inappropriate
Content
-­‐
Posts
which
are
disrespecmul
to
other
users,
profanity,
adver6sing,
naming
and
shaming
and
generally
causing
discord
or
disharmony
on
the
forum.
Self
policing
mechanisms

56. How
is
this
growing?
Also
NPS
=
73
(Apple
=
79)

57. Labor
/
Knowledge
Work
Influence
Capital
Assets
Data
Institutions
vs
Algorithms

58. Anant
Agarwal
“We
can
watch
how
many
a1empts
students
made
before
they
got
an
exercise
right,
and
if
they
got
it
wrong,
what
they
used
to
try
to
find
a
solu6on.
Did
they
go
to
the
textbook,
go
back
and
watch
the
video,
go
to
the
forum
and
post
a
ques6on?”
About
1
month
ago
Data
+
Algorithms
for
Knowledge
Management

59. Elinor
Ostrom
“It's
a
problem,
it's
just
not
necessarily
a
tragedy
...
The
problem
is
that
people
can
overuse
[a
shared
resource],
it
can
be
destroyed,
and
it
is
a
big
challenge
to
figure
out
how
to
avoid
that.”
About
2
years
ago
Community
+
Institutions
for
Knowledge
Management

60.
Knowledge
from
Crowds
–
Be1er
with
Ins6tu6ons
+
Algorithms
h1p://goo.gl/q1DNL
Shaun
Abrahamson
@shaunabe