Seminario eMadrid/SHEILA sobre "Analítica del Aprendizaje". Panorama general de las actividades de Learning Analytics en la UC3M. Carlos Delgado Kloos, Pedro Muñoz Merino. 21/10/2016.

1. An Overview of Learning
Analytics Activities in UC3M
Carlos Delgado Kloos, Pedro J. Muñoz-Merino
Univ. Carlos III de Madrid, www.it.uc3m.es
Red eMadrid, www.emadridnet.org
UNESCO Chair on
Scalable Digital Education for All
Spain
United Nations
Educational, Scientificand
Cultural Organization

2. Knowledge and Data
"Quite simply,
knowledge is the engine of our economy.
And data is its fuel."
Neelie Kroes
Former Vicepresident
European Commission,
Responsible for Digital Agenda
C. Delgado Kloos, P.J. Muñoz-Merino Madrid, 2016-10-21
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3. Precious Data
"Data is a precious thing
and will last longer
than the systems themselves."
Tim Berners-Lee
Director W3C
Inventor of the WWW
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4. Privacy
www.aclu.org/ordering-pizza
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5. Data is Precious
Medical Sector
 Data needed for the well-being of the patient
 For medical research
Educational Sector
 Educational Data Mining
 Learning Analytics
 Useful for whom?
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6. Learning & Academic Analytics
www.educause.edu/ero/article/
penetrating-fog-analytics-learning-and-education
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7. Compromise
Balance
 Accountable, justified use
 Privacy
 Security
Regulation and policies needed
 Briefgeheimnis
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8. Legal Background in Spain
 Spanish Constitution (1978)
 Article 18:
Right to personal privacy
 AEPD: Agencia Española
de Protección de Datos
 Since 1993
 Data Protection Directive
95/46/EC
 LOPD: Ley Orgánica de
Protección de Datos 15/1999
 Protects personal privacy
rights related to data
 Agencia de
Protección de Datos de la
Comunidad de Madrid
 2001-2012
 Reglamento de Medidas de
Seguridad (RD 1720/2007)
 Data security measures
 Universities: Comisión LOPD
 Gives recommendations
and takes care of law
at the university
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9. Policies
The legal background is in place
The structures operative
However, the field is moving fast
Policies have to be put in place for handling
the different scenarios that are coming up
 Awareness, training, involvement of many stakeholders, ...
 International collaboration
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10. Adoption at UC3M
First pilots
 0 Courses (Khan Academy)
 SPOCs (Open edX)
In progress
 All SPOCs
 edX
 Moodle
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11. 0 Courses
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12. JavaMOOC
Introduction to Programming with Java –
Part 1: Starting to Code with Java
Over 250.000 learners from 208 countries in 3 editions
uc3mx.es
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13. Active Research Projects on LA
 SHEILA: European Commission,
2016-2018
 MOOC-Maker: European
Commission, 2016-2019
 eMadrid: Regional Government
of Madrid, 2014-2018
 Smartick: Project with company.
2015-2016
 Aplicaciones de analítica y
sistemas de recomendación:
Project with company, 2016-
2017
 Simlap: Spanish Ministry of
Economy, 2014-2016
 mapaTIC: Spanish Ministry of
Education. 2015-2016
 RESET: Spanish Ministry of
Economy. 2015-2017
 SNOLA (Spanish Network of
Learning Analytics):
Spanish Ministry of Economy
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14. Research lines at UC3M
1) Collection of events and interoperability
2) Inference of intelligent information
3) Visualization systems in e-learning platforms
4) Evaluation of learning experiences
5) Adaptive learning. Personalized learning
6) Prediction and recommendation systems
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15. 1) Collection of events and
interoperability
PJ Muñoz-Merino, M Wolpers, K Niemann, M Friedrich,
A Pardo, C Delgado Kloos, M Muñoz-Organero:
“CAM in the Semantic Web world,”
6th Internat. Conf. on Semantic Systems, 2010 I-SEMANTICS ’10
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16. 2) Inference of intelligent
information
Image taken from
http://www.datadial.net/blog/index.php/2011/08/24/why-
your-website-isnt-as-fast-as-it-should-be/
Learning
profiles
Behaviours
Meta-
cognitive
skills
Emotions
Efectiveness
Efficiency
Interest
Gamification
interest
Machine
learningPedagogy
Expert
Knowled.
Semantics
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17. 2) Example: Skill Modelling
Know if a student has
mastered certain skills
• Application of
probabilistic models such as
Item Response Theory (IRT),
Bayesian Networks (BN),
or Knowledge Spaces (KS) and
proposal of new techniques
Master thesis:
Ruth González Novillo.
Supervisors: PJ Muñoz-Merino,
C Delgado Kloos
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18. 2) Example: Effectiveness
PJ Muñoz-Merino, JA Ruipérez-Valiente, C Alario-Hoyos,
M Pérez-Sanagustín, C Delgado Kloos:
"Precise effectiveness strategy for analyzing the effectiveness of
students with educational resources and activities in MOOCs",
Computers in Human Behavior, vol. 47, pp. 108–118 (2015)
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19. 2) Example: Emotions
D Leony, PJ Muñoz-Merino, A Pardo, C Delgado Kloos:
“Provision of awareness of learners’ emotions through
visualizations in a computer interaction-based environment”,
Expert Systems With Applications, vol. 40, no. 3 (2013), pp. 5093-
5100
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20. 2) Example: Gamification
D Leony, PJ Muñoz-Merino, A Pardo, C Delgado Kloos:
“Provision of awareness of learners’ emotions through
visualizations in a computer interaction-based environment”,
Expert Systems with Applications, 40:3 (2013), 5093-5100
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21. 3) Visualization Systems:
ALAS-KA
JA Ruipérez-Valiente, PJ Muñoz-Merino, D Leony, C Delgado Kloos:
“ALAS-KA: A learning analytics extension for better understanding
the learning process in the Khan Academy platform”,
Computers in Human Behavior, 47 (2015), 139-148
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22. 3) Visualization Systems:
ANALYSE
Main people involved:
Héctor Pijeira Díaz, Javier Santofimia, Jaime Alzola, Javier
Orcoyen, José A. Gascón Pinedo, José A. Ruipérez Valiente,
Pedro J. Muñoz-Merino, Carlos Delgado Kloos
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23. 3) Visualization Systems:
Google Course Builder
M Marciel, F Michelinakis, R Fanou, PJ Muñoz-Merino:
“Enhancements to Google Course Builder: Assessments
Visualisation, YouTube Events Collector and Dummy Data
Generator”, SINTICE conference (2013)
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24. 4) Evaluation of
Learning Experiences
Examples: universities,
high schools, companies
For the evaluation, we use:
system indicators, tests,
surveys, interviews, etc.
Evaluated: students, tools,
the learning process, …
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25. 5) Adaptive learning.
Personalized learning
PJ Muñoz-Merino, C Delgado Kloos, M Muñoz-Organero, A
Pardo:
"A Software Engineering Model for the Development of Adaptation
Rules and its Application in a Hinting Adaptive E-learning System",
Computer Science and Information Systems, 12:1, 203-231(2015)C. Delgado Kloos, P.J. Muñoz-Merino Madrid, 2016-10-21
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26. 5) Adaptive learning.
Personalized learning
PJ Muñoz-Merino, M Fernández Molina, M Muñoz-Organero,
C Delgado Kloos: “An Adaptive and Innovative Question-driven
Competition-based Intelligent Tutoring System for Learning”,
Expert Systems with Applications, 39:8 (2012), 6932-6948
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27. 6) Prediction and
Recommendation Systems
• Prediction about
• Learning gains
• Certificate earners
• Social activity
• Cheating
• System behaviours
• Recommenders about
• Resources
• Users
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28. Design and implementation of
telematic applications
• E-learning applications
• Processing efficiency
• Technologies
• Web: J2EE, PHP, Django, …
• Data bases: mysql, MongoDB, Oracle, …
• Client: HTML, JavaScript, CSS, …
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29. Conclusion
Advancing in new territories
Involvement of stakeholders from many fields
 Law and policies
 Teaching methodologies
 Educational research
 Practice
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30. C. Delgado Kloos, P.J. Muñoz-Merino Madrid, 2016-10-21
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