Jose Luis Fernandez-Marquez (UNIGE) presenting CCL tracker framework at the Citizen Cyberlab Summit, 17-18 September 2015, University of Geneva (UNIGE).

1. CCLTracker
Framework
Monitoring
users
learning
and
ac7vity
in
web
based
ci7zen
science
projects
Jose
Luis
Fernandez-­‐Marquez
Joseluis.fernandez@unige.ch

2. Outline
• Mo7va7on
• Background
(Google
Analy7cs)
• CCLTracker
Framework
• Conclusions
• Experiment
and
demo

3. Mo7va7on
Bounce
rate,
pages
views,
avg.
7me
per
session,
etc…
might
not
be
relevant
to
measure
user
engagement,
or
relevant
in
our
ci7zen
science
project.

4. Google
Analy7cs
• Audience
• Acquisi7on
• Behaviour

5. Audience:
Par7cipa7on
16.000
sessions
8.000
users
108
countries
–
90
languages
Avg.
of
400
sessions
per
day

6. Audience:
Who?
+
browser,
OS,
and
mobile
or
PC
connec7on

7. Acquisi7on:
where
do
they
come
from?

8. Acquisi7on:
where
do
they
come
from?

9. Acquisi7on:
Evalua7ng
dissemina7on
ac7vi7es
Itc.ua
1st
Release
Twi^er
Reddit
Post

10. Behavior

11. Segmenta7on,
crossing
data
and
adding
filters
• Give
me
previous
stats
focusing
on
visitors
coming
from
reddit.
• Give
me
previous
stats
focusing
on
female
visitors,
between
18
and
24
years,
linux
users,
living
in
Switzerland.
• Infinite
number
of
possible
combina7ons.

12. Segmenta7on,
crossing
data
and
adding
filters
• Top
10
countries
by
Female
users.
• Top
10
referrals
(reddit,
facebook,
…)
gathering
female
users.
• Top
10
referrals
gathering
visitors
who
immediately
run
away
from
the
site.

13. Why
do
we
need
anything
else?

14. Bounce
rate,
pages
views,
and
avg.
4me
per
session
might
not
be
relevant
for
a
ci7zen
science
project.
– We
need
to
know
the
ac7ons
performed
in
the
site
to
measure
par7cipants
contribu7on.
– We
need
to
be
able
to
make
public
analy7c
data.
– We
need
to
be
able
to
create
advance
data
aggrega7on.
I.e.
clustering
analysis,
advance
engagement
func7ons.

15. CCLTracker
framework
CCLTracker
JS Library
Google
Analytics
Google Tag
Manager
RGA Library
R
Monitoring Storing, aggregating,
reporting
Advance Aggregation
and reporting
Google
Super Proxy

16. CCLTracker
events
-­‐ Is
the
user
scrolling
down
on
the
web
site
(0%,
25%,50%,75%100%)
-­‐
is
the
user
clicking
new
projects,
about,
forum,
etc?

17. CCLtracker
events

18. CCLTracker
events

19. Segmenta7on,
crossing
data
and
filtering
• Stats
only
for
par7cipants
who
has
properly
installed
the
CERN
VM.
(segmenta7on)
• Comparing
two
segments
(e.g.
all
sessions
vs
sessions
running
the
virtual
machine.

20. Segmenta7on,
crossing
data
and
filtering

21. Segmenta7on,
crossing
data
and
filtering
• Most
common
web
API
errors
by
browser
(crossing
data,
filtering)

22. Segmenta7on,
crossing
data
and
filtering
• Top
more
ac7ve
users.

23. Segmenta7on,
crossing
data
and
filtering
• All
ac7ons
by
a
given
user

24. Advance
Data
Aggrega7on
1 2 3 4 5 6 7 8 9 10 12
Number of days
%ofusers
05101520253035
29.58
17.63
9.5210.18
7.38
2.88
3.87
5.31
3.32
1.84
4.064.43
• Engagement

25. Advance
Data
Aggrega7on

26. Advance
Aggrega7on
Visitors
per
age
group
Visitors
successful
running
VM
per
age
group
0
10
20
30
40
50
60
18-­‐24
25-­‐34
35-­‐44
45-­‐54
55-­‐64
>65
%
of
sessions
Users'
age
User
engagement
per
age
group

27. Advance
Data
Aggrega7on
Desired
Flow
of
ac7ons

28. Advance
Data
Aggrega7on

29. Why
do
we
want
all
this
data?
• Increase
the
number
of
par7cipants
• Increase
par7cipants’
engagement.
• Improve
the
navigability,
and
accessibility
of
the
website.
• Improve
users’
learning
experience.
Are
users
improving
the
quality
of
their
contribu7ons?

30. What
did
we
learn
using
CCLTracker?
• Transla7on
to
different
languages
is
important
to
reach
a
large
audience.
E.g.
Russian
referral.
• Addressing
technology
sec7ons
in
newspapers.
• Web
site
naviga7on
is
not
trivial.
• Low
engagement.
• Low
female
par7cipa7on.

31. 2nd
CERN
Public
Compu7ng
Challenge
• Increase
the
number
of
par7cipants:
– Addressing
female
par7cipa7on.
– Transla7ng
the
website
to
different
languages.
– Pos7ng
the
informa7on
in
data
hubs,
scien7fic
sec7ons
of
newspapers,
etc…
• Segmen7ng
data
by
different
level
of
engagements:
– Visitors
who
do
not
run
the
VM.
– Visitors
who
compute
10
jobs,
20
jobs,
…
n
jobs.
– Visitors
who
compute
for
1
h,
5
h,
…
n
hours.
– Visitors
who
par7cipate
over
the
whole
challenge…

32. “top
10%
of
contributors
responsible
for
almost
80%
of
total
classifica7ons.”
Open
ques7on