Presentacion "PlanCeibal onthe Big Data runway" (Cecilia Marconi, Fundación Ceibal)
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Plan Ceibal on the Big Data runway The 20th Iberoamerican Congress on Pattern Recognition (CIARP 2015) Montevideo, 11th November, 2015
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Transcript of Presentacion "PlanCeibal onthe Big Data runway" (Cecilia Marconi, Fundación Ceibal)
Plan Ceibal on the Big Data runway
The 20th Iberoamerican Congress on Pattern Recognition (CIARP 2015)
Montevideo, 11th November, 2015
• 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
Educa4onal centers 3.130
Op4cal fiber & Videoconference
1.284
Public Spaces 304
Others 184
Ceibal Internet Network
Op4cal fiber DSL
Digital educa4onal content
Educa4onal Resources
On-‐line Evalua4on Digital library LMS
Teaching English
Math Adap4ve PlaSorm
Progamming
Robo4cs & digital labs
What kind of data do we have?
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
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
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
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
And here we are….
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..
?
Some current studies…
Statis>cs asocia>on Causal Inference
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
#2 Laptops-‐survival analysis. Inquire whether the sociodemographic characteris>cs of the students affect the survival >me of the XO
0.00
0.25
0.50
0.75
1.00
0 500 1000 1500 2000analysis time
context5 = Desfavorable context5 = Favorablecontext5 = Medio context5 = Muy desfavorablecontext5 = Muy favorable
Kaplan-Meier survival estimates
The hazard rate for "Muy Desfavorable” (unfavorable) is 49% higher than "Muy Favorable” (favorable)
(Preliminary results)
#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.
Next steps….
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
4
7/18Unsupervised Pattern Recognition (Clustering)
Problem formulation
8/18Unsupervised Pattern Recognition (Clustering)
Problem formulation
How many clusters?
Four ClustersTwo Clusters
Six Clusters
Thanks
