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Presentacion "Plan Ceibal on the Big Data runway" (Cecilia Marconi, Fundación Ceibal)


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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.

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Presentacion "Plan Ceibal on the Big Data runway" (Cecilia Marconi, Fundación Ceibal)

  1. 1. Plan  Ceibal  on  the  Big  Data  runway     The 20th Iberoamerican Congress on Pattern Recognition (CIARP 2015) Montevideo, 11th November, 2015
  2. 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. 3. Educa4onal     centers   3.130   Op4cal  fiber  &   Videoconference   1.284   Public     Spaces   304   Others   184   Ceibal  Internet  Network   Op4cal  fiber   DSL  
  4. 4. Digital  educa4onal  content   Educa4onal  Resources   On-­‐line  Evalua4on   Digital  library   LMS   Teaching  English   Math  Adap4ve     PlaSorm   Progamming   Robo4cs  &  digital  labs  
  5. 5.   What kind of data do we have?
  6. 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. 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. 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. 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. 10.   And here we are….
  11. 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. 12. Some  current  studies…   Statis>cs  asocia>on   Causal  Inference  
  13. 13. h]p://>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. 14.         #2      Laptops-­‐survival  analysis.  Inquire  whether  the  sociodemographic   characteris>cs  of  the  students  affect  the  survival  >me  of  the  XO 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. 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. 16. Next steps….  
  17. 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. 18. Comments  &  sugges>ons              Cecilia  Marconi   Center  for  Research  -­‐  Ceibal  Founda>on  
  19. 19. Thanks