2013 07 05 (uc3m) lasi emadrid cdk uc3m introduccion

1. LASI-Spain eMadrid
Carlos Delgado Kloos
Univ. Carlos III de Madrid, www.it.uc3m.es
Red eMadrid, www.emadridnet.org

2. LASI
SOLAR: Society for Learning Analytics Research
Learning Analytics Summer Institute
 Stanford U, 1-5 July 2013
 LASI-local events
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3. LASI-local
Local events around the world:
 Aalborg (.dk)
 Amsterdam (.nl)
 Boston (.us)
 Edinburgh (.uk)
 Granada (.es)
 Hong Kong (.hk)
 Lyon (.fr)
 Madrid (.es)
 Pretoria (.za)
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4. LASI-Spain eMadrid (ES)
Organized by eMadrid at UC3M
 Morning Session:
Stanford U presentations from previous days
 Afternoon Session:
Local presentations at UC3M
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5. LASI-Spain eMadrid (ES)
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6. LASI-Spain eMadrid (ES)
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7. LASI-Spain eMadrid, 2013-07-05
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What are they
thinking about?
How
boring!
I don't get
anything!
How cool!
Are they
following?
We'll find out at
the exam

8. Fast Feedback Loop
Waiting until the exam is too late
Fast feedback cycle important for learning
Remediation when it most effective
Probe information during the learning process
Adapt teaching to learning
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9. (Big) Data
Higher
 number of people (MOOCs)
 number of variables (pervasive technologies)
 number of time points
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10. Learning Interactions
in the Physical World
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prof student student
colab
lecture
tutor
read practice
exam practiceassess

11. Analytics
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12. No Data
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student
read

13. Data
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student
read
data

14. Data
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student
read
data

15. Learning Interactions
in the Digital World
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prof student student
colab
read
lecture
practice
tutor
exam practiceassess

16. Analytics
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data
info
action
visualization

17. Data
What kind?
 Clicks
 Times
 Users
 Activity
 Advance
 Accomplishment of exercises
 Use of hints
 Social behaviour
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18. Information
What kind?
 Present status
 Prediction about
the future
 Plans for changing
the future
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19. Information
For whom?
 Learner (pilot)
 Teacher (air traffic controller)
 Instructional designer
 Curriculum planner
 Institution
 Country authority
 Employer
 Tool developer
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20. Processing
Classical test theory
Item response theory
Aggregation
Averages
Classifiers
Markov models
…
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21. Visualization
a
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22. Action
Repetition
Reinforcement
Remediation
Rescheduling
Social interaction
…
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23. Issues
Privacy
Causality
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!

24. Privacy
By capturing data from persons,
one has to be very careful with
how and what data is captured,
and how it is used, etc.
The correct legal measures
have to be taken
Who owns the data?
 When it is raw?
 When it is processed?
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25. Causality
The brain is a big unknown
We know very little about
how learning works
By correlating a few data,
we are just scratching the surface
Learning Analytics is no silver bullet
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26. Conclusion
Turn the learning process into a controlled process,
as many other processes (business, health, web)
Relevant are speed of feedback and amount of data
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27. Talks (be punctual!)
15:15-15:45: Á. Serrano (UCM): "Game analytics
and educational videogames: Can we know how
many students learn while playing videogames? "
15:45-16:15: R. Crespo, I. Gutiérrez (UC3M):
"Learning analytics support for just-in-time teaching"
16:15-16:45: P. Muñoz Merino (UC3M): "Evaluation
in e-learning platforms using learning analytics"
16:45-17:00: Break
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28. Talks (be punctual!)
17:00-17:30: G. Robles, J. González Barahona (URJC):
"Lessons learned from ten years of software analytics
that could be helpful for learning analytics"
17:30-18:00: J. García Zubía (U Deusto):
"Learning analytics and remote experimentation:
First experiences with Weblab-Deusto"
18:00-18:45: Live from Stanford U:
Plenary: "Chief Data Scientists"
Chair: Dragan Gasevic (Athabasca U)
Speaker: Piotr Mitros (edX)
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