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Towards the Development of 
a Learning Analytics module 
in Open edX 
Presenter: Pedro J. Muñoz-Merino 
e-mail: pedmume@it.uc3m.es, Twitter: @pedmume 
Javier Santofimia Ruiz, Héctor J. Pijeira Díaz, José A. Ruipérez-Valiente, Pedro J. Muñoz- 
Merino, Carlos Delgado Kloos 
Universidad Carlos III de Madrid, Spain
Learning Analytics Cycle 
Generation 
and data 
capture 
Technological problems arise 
Transform 
raw data 
Visualizer 
Recommender 
Low level Report generator 
data Higher level 
information 
What data is 
necessary 
What to do with high level 
information 
What to obtain and 
How to do it 
Correlations, 
causality, 
clustering, 
prediction 
2 
Do Optional Activities Matter in 
Virtual Learning Environments? 
EC-TEL 2014
3 
Different systems 
• Depending on the system, there are 
different types of events and different type 
of information that can be reused but there 
are specificities: 
– Programming environment for learning C 
– Khan Academy 
– edX
4 
Inference of information (I) 
• Type of information 
– Progress 
– Efficiency, time distribution 
– Emotions 
– Optional activities 
– Students behaviours (try abuse, hint 
avoidance, unreflective) 
– Gamification habits
5 
Inference of information (II) 
• Based on 
– Pedagogical theories 
– Best practices 
– Adaptation of models from other systems 
– Indicators that can predict important 
information
6 
Visualizations
7 
Interpretation of the information 
and actions 
• Combination of different parameters and 
visualizations 
• Relationship mining 
• Determining causes 
• Prediction 
– Learning gains 
– Social activity 
• Recommendation
Towards the Development of 
a Learning Analytics module 
in Open edX 
Presenter: Pedro J. Muñoz-Merino 
e-mail: pedmume@it.uc3m.es, Twitter: @pedmume 
Javier Santofimia Ruiz, Héctor J. Pijeira Díaz, José A. Ruipérez-Valiente, Pedro J. Muñoz- 
Merino, Carlos Delgado Kloos 
Universidad Carlos III de Madrid, Spain

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Towards the Development of a Learning Analytics extension in Open edX

  • 1. Towards the Development of a Learning Analytics module in Open edX Presenter: Pedro J. Muñoz-Merino e-mail: pedmume@it.uc3m.es, Twitter: @pedmume Javier Santofimia Ruiz, Héctor J. Pijeira Díaz, José A. Ruipérez-Valiente, Pedro J. Muñoz- Merino, Carlos Delgado Kloos Universidad Carlos III de Madrid, Spain
  • 2. Learning Analytics Cycle Generation and data capture Technological problems arise Transform raw data Visualizer Recommender Low level Report generator data Higher level information What data is necessary What to do with high level information What to obtain and How to do it Correlations, causality, clustering, prediction 2 Do Optional Activities Matter in Virtual Learning Environments? EC-TEL 2014
  • 3. 3 Different systems • Depending on the system, there are different types of events and different type of information that can be reused but there are specificities: – Programming environment for learning C – Khan Academy – edX
  • 4. 4 Inference of information (I) • Type of information – Progress – Efficiency, time distribution – Emotions – Optional activities – Students behaviours (try abuse, hint avoidance, unreflective) – Gamification habits
  • 5. 5 Inference of information (II) • Based on – Pedagogical theories – Best practices – Adaptation of models from other systems – Indicators that can predict important information
  • 7. 7 Interpretation of the information and actions • Combination of different parameters and visualizations • Relationship mining • Determining causes • Prediction – Learning gains – Social activity • Recommendation
  • 8. Towards the Development of a Learning Analytics module in Open edX Presenter: Pedro J. Muñoz-Merino e-mail: pedmume@it.uc3m.es, Twitter: @pedmume Javier Santofimia Ruiz, Héctor J. Pijeira Díaz, José A. Ruipérez-Valiente, Pedro J. Muñoz- Merino, Carlos Delgado Kloos Universidad Carlos III de Madrid, Spain