Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. http://www.fundacionceibal.edu.uy/

1. Plan
Ceibal
on
the
Big
Data
runway
The 20th Iberoamerican Congress on Pattern Recognition
(CIARP 2015)
Montevideo, 11th November, 2015

2. • Social
Inclusion
• Equality
of
Opportuni4es
• Teaching
and
Learning
700.000
users
with
laptops
or
tablets
Plan
Ceibal
is
not
an
ICT
Program
or
Laptops
Program

3. Educa4onal
centers
3.130
Op4cal
fiber
&
Videoconference
1.284
Public
Spaces
304
Others
184
Ceibal
Internet
Network
Op4cal
fiber
DSL

4. Digital
educa4onal
content
Educa4onal
Resources
On-­‐line
Evalua4on
Digital
library
LMS
Teaching
English
Math
Adap4ve
PlaSorm
Progamming
Robo4cs
&
digital
labs

5.
What kind of data do we have?

6. Ceibal
core
Informa4on
system
Matrix
of
data
Source
Dimension
-­‐
Variables
Size
of
data
User´s
Socio-­‐demographic
features
Age
Gender
Socio-­‐
cultural
context
Loca>on
Physical
Infrastructure
delivered
Internet
access
Device
ID
Model
-­‐
laptop
Date
delivered
Ticket
Tracking
Date
of
Failure
Type
of
Failure
Support
service
+700K
users
+42.000
>ckets
per
month

7. Source
Dimension
-­‐
Variables
Size
of
data
Matrix
of
data
Monitoring
and
performance
of
IT
infrastructure
Performance
analysis
of
IT
schools
infrastructure
+3500
Buildings
&
other
facili>es
Internet
traffic
VC
traffic
#
Client
connec>ons
Network
availability
Connec>vity
Hardware

8. Tracker
System
Computer
usage
Time
of
usage
Opera>ng
system
Applica>ons
Amount
of
users
+50
schools
+3000
students
Source
Dimension
-­‐
Variables
Size
of
data
Matrix
of
data
School
Servers
Logs
Individual
internet
ac4vi4es
Internet
Performance
+3500
buildings
&
other
facili4es

9. Ceibal's
Math
Adap4ve
PlaSorm
Performance
25.420.060
excercises
108.924
users
Topic
Exercises
completed
Success
rate
Time
of
usage
Autonomous
work
On-­‐line
Evalua4on
Teaching
English
Learning
Assigment
teachers
Remote
teacher´s
Ins>tute
Class
Videos
Source
Dimension
-­‐
Variables
Size
of
data
Matrix
of
data
+145.525
users
+537.616
comments
+292.099
submissions
Comments
posted
Submissions
Ac>ve
Users
Files
Uploaded
IP
Adress
Learning
Management
System
Performance
English
Adapta>ve
Test
+70.000
anual
test
+315
RT
+18Ins>tutos
+105.600
Videos

10.
And here we are….

11. Learning
Analy>cs
Business
Inteligence
Unstructred
Data
Structured
Data
How
can
we
improve
the
integra4on
of
the
different
data
sources
in
a
more
comprehensive
and
meaningful
way?
Hadoop/Spark/
GraphLab/Watson..
?

12. Some
current
studies…
Statis>cs
asocia>on
Causal
Inference

13. h]p://www.fundacionceibal.edu.uy/en/news/learninganaly>cs-­‐educa>on-­‐edtech-­‐and-­‐
bigdata-­‐challenging-­‐a]rac>ve-­‐opportunity
Further
ques4ons:
• Correla>on
PAM
>
Academic
Performance
• Clustering
of
teacher´s
profile
>
PAM
intensity
Compare
means
between
t0
and
t1
by
loca>on
The
more
powerful
the
network
infrastructure
the
higher
intensity
of
use
in
PAM
(completed
ac4vi4es
per
day).
#1

14.
#2
Laptops-­‐survival
analysis.
Inquire
whether
the
sociodemographic
characteris>cs
of
the
students
affect
the
survival
>me
of
the
XO
0.000.250.500.751.00
0 500 1000 1500 2000
analysis time
context5 = Desfavorable context5 = Favorable
context5 = Medio context5 = Muy desfavorable
context5 = Muy favorable
Kaplan-Meier survival estimates
The
hazard
rate
for
"Muy
Desfavorable”
(unfavorable)
is
49%
higher
than
"Muy
Favorable”
(favorable)
(Preliminary
results)

15. #3
Random
Assigment
(Ins>tutes
/
Remote
Teachers)(RA)
STUDENT
PERFORMANCE
On-­‐line
Adapta>ve
Test
Classroom
Observa>on
On-­‐line
Surveys:
-­‐Classroom
Teachers
-­‐Remote
Teachers
-­‐Students
-­‐School
Director
-­‐
Administra>ve
Informa>on
&
LMS
Data
First
phase:
Second
phase:
Third
phase:
How
we
can
improve
the
impact
of
the
Ceibal
en
Ingles
Program?
Studies
on
the
quality
of
English
teaching:
characteris>cs
and
teaching
prac>ces,
classroom
interac>ons
and
learning.

16. Next steps….

17. GOAL
Use
advanced
analy4c
techniques
to
understand
and
help
target
instruc4onal,
curricular
and
support
resources,
to
enhance
the
achievement
of
specific
learning
goals.
Big
chance
to
study
behaviors
of
en4re
students
genera4on
To
create
technical
and
human
capabili>es
in
order
to
develop
a
research
area
for
Learning
Analy>cs
To
create
network
of
Universi>es,
Instiu>ons,
Experts,
Reasercher
to
work
colabora>ve
Pa]ern
recogni>on
in
educa>onal
field:
use
of
technology
and
educa>onal
content,
clustering
teacher´s
profile
Problem formulation

18. Comments
&
sugges>ons
Cecilia
Marconi
cmarconi@ceibal.edu.uy
fundacion@ceibal.edu.uy
Center
for
Research
-­‐
Ceibal
Founda>on

19. Thanks