The document discusses how Plan Ceibal in Uruguay can utilize big data analytics. Plan Ceibal has deployed infrastructure like laptops and tablets to over 700,000 students and teachers, generating large amounts of data daily. It outlines key data sources and challenges like lack of integration. A case study examines how network performance correlates with math platform usage. Next steps proposed include systematizing data collection, defining targets, and creating capabilities for data warehousing, analysis and visualization to inform decision making.

1. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
How Can Plan Ceibal Land into the Age of Big
Data?
Mat´ıas Mateu
mmateu@ceibal.edu.uy
@Mateu Matias
July 23, 2015

2. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal

3. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal
2 Motivation

4. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources

5. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges

6. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions

7. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2

8. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Overview
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

9. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

10. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Uruguay
Uruguay
3.4 M people
GDP per capita 16,400 USD (2013) ranking 44th.

11. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Uruguay
Uruguay’s best well known
Football Association
(Soccer)

12. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Uruguay
Uruguay’s best well known
Football Association
(Soccer)
Asado (Barbacue)

13. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Uruguay
Uruguay’s best well known
Beaches and Resorts:
Punta del Este

14. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Uruguay
Uruguay’s best well known
Beaches and Resorts:
Punta del Este
Yerba Mate

15. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Uruguay
Uruguay’s best well known
Wines: Tannat

16. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Introduction
Plan Ceibal
Plan Ceibal’s Presentation - Institutional Video

17. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Motivation
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

18. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Motivation
Deployed infrastructure
Laptops and tablets
+700,000 students and professors
Broadband connectivity
+3000 educational facilities with wireless connectivity
Almost 90 % of students with broadband access in school
facilities
More than 70 % of students with broadband access at
households

19. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Motivation
Deployed infrastructure cont.

20. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Motivation
Systems deployed
Platforms

21. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Motivation
Systems deployed... Generate data
There is a huge opportunity to use it to support strategic decisions
of the Pedagogical/Technological Program in Uruguayan Schools,
help measure efficacy of the technologies in hands of students and
finally to develop real time, personalized feedback for students and
professors to improve and accelerate their learning process

22. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Motivation
The presented paper
Describes the main data sources, dimensions and variables
Presents some of the challenges and questions that arise to
take profit from data
Shows a Case Study
Gives a possible strategy towards implementation of a
framework and institutional process for Data Analytics
ID: Data analytics 60119

23. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Data Sources
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

24. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Data Sources
Data Matrix

25. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Data Sources
Data Generation
Size of Daily Generated Data
Source Size (Mega Bytes)
Zabbix 200
CRM 4
Tracker 6
PAM activity 10
Internet activity 150
Total 370

26. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Challenges
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

27. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Challenges
Challenges
Great amount of known and unknown variables
(hundreds)

28. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Challenges
Challenges
Great amount of known and unknown variables
(hundreds)
Lack of Integration

29. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Challenges
Challenges
Great amount of known and unknown variables
(hundreds)
Lack of Integration
Lack of a common processing and visualization
framework

30. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Challenges
Challenges
Great amount of known and unknown variables
(hundreds)
Lack of Integration
Lack of a common processing and visualization
framework
Lack of traceability

31. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Key Questions
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

32. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Key Questions
Key Questions
What are the key parameters, significant variables and required
data sources to include in the integration and exploitation stages?

33. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Key Questions
Key Questions
What are the key parameters, significant variables and required
data sources to include in the integration and exploitation stages?
How can we improve integration of the different data sources
in a more comprehensive and meaningful way?
How to enable interoperability and consistency between
information and variables retrieved from different data sources
(i.e: the unit of analysis in some cases are schools, classrooms
or individual based information)?
What are the more reliable analytical techniques to identify
strong correlations amongst key variables?
How can the integration of the different data sources be
applied to better understand ways of improving institutional
and pedagogical strategies?

34. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

35. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 1
Case Study - First Phase
Motivation
Since 2013 Plan Ceibal has considered the adoption of PAM
(Spanish acronym of Adaptive Maths Platform) among
Professors and students as strategic
Since 2014 Plan Ceibal begun to optimize wifi performance,
called High Performace Network (HPN) in every urban
school facility

36. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 1
Methodology
Hypothesis
1 HPN will facilitate a higher amount of exercises completed by
students in PAM
2 The social-demographic features (metropolitan vs. interior
urban, and socio-cultural context) affect the use of PAM
Research questions
1 To what extent does network performance correlate with PAM
use?
2 To what extent do the social-demographic features impact the
relationships between HPN and PAM use?

37. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 1
Methodology cont.
Key variables
PAM use (number of excercises per student in a given period)
HPN: logical variable
Socio-demographic characteristics (index used at Government
level)
MAC: Assistant Teacher to support use of PAM
Universe and Sample
100 schools with HPN during 2014, 13800 students from 4th
to 6th level of primary
Random and stratified by sociodemographic context sample:
18 schools with 3,823 students

38. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 1
Preliminary Results
An increase of 35.6% active PAM users has been detected after
HPN was installed
Number of PAM active users before and after HPN deployment
Before HPN After HPN
# PAM active users 806 1093
# activities 53179 67523

39. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 2
Case Study - Second Phase
Methodology
A control group was taken: a set of schools without HPN
during 2014
Period of time restricted to second semester of 2014
Factor Analysis based on OLS Multivariate Model to find
significant impacts in context variables
Dependent variable: Average daily exercises completed in
PAM per student
Independent variables:
HPN
Presence of MAC (Ceibal’s Assistant) professor in school
Geographical emplacement of school (urban interior vs.
Montevideo)
Socio-cultural context of school

40. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 2
Results of multivariate analysis
Factor analysis
All coefficients are statistically significant at p < .05
HPN impact is not significant when it is controlled by
sociodemographic contexts

41. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 2
Case study synthesis
In Schools with MAC support, favorable context and urban interior
(bivariate analysis: Average Exercices in PAM and HPN) we
identified a significant impact or causal effect. That is to say that
given favorable conditions, HPN is something students profit from

42. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Case Study
Phase 2
Further questions
Technology
Can we find correlations between PAM intensity of use and
device performance?
Can we find correlations between the use of exercise in PAM
and the academic performance of students in Math?
What are the learning outcomes of exercising in PAM?
Can the clustering of teacher’s profile illustrate their influence
in PAM’s intensity of use?
Context
To pursue an expanded analysis exploring the impact of
factors such as context, geographical location or provision of
Teaching Assistants

43. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Next Steps
1 Introduction
Uruguay
Plan Ceibal
2 Motivation
3 Data Sources
4 Challenges
5 Key Questions
6 Case Study
Phase 1
Phase 2
7 Next Steps

44. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Next Steps
Next steps towards Big Data in Education
1 Need for a systematization of the duty of gathering,
processing and analyzing data
2 Define targets and Planning (i.e: motivation, engagement,
compromise)
3 Create Institutional capabilities to:
design and implement data library and data-warehouse
generate technical skills (measurement and analysis)
develop or integrate visualization tools
incorporate decision making process
Implement and evaluate periodically

45. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Authors and Collaborators
Authors
Martina Bail´on
Mauro Carballo
Crist´obal Cobo
Soledad Magnone
Cecilia Marconi
Mat´ıas Mateu
Hern´an Susunday
Collaborators
Helena Rovner
Daniel Castelo
Juan Pablo Gonz´alez
Fiorella Haim
Claudia Brovetto
Leonardo Castellucio

46. How Can Plan Ceibal Land into the Age of Big Data? Data Analytics - IARIA 2015. Nice, France
Thanks! Questions?