Pushing the awareness envelope

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Presentation given at the EEE meeting in Barcelona 13/09/2012
By Derick Leony and Israel Gutiérrez

Published in: Technology, Education
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Pushing the awareness envelope

  1. 1. Pushing the awarenessenvelope: from interactions to emotions Israel Gutiérrez Derick Leony University Carlos III of Madrid
  2. 2. Awareness across spaces with learning analytics Data Process / Inter- collection Analysis ventions• Subject: Teachers and students• Tool: Learning analytics with traces across spaces• Objective: • Recommend: classON and affective recommender
  3. 3. Guess what? Number of months to reach 1 million usersCompany 1Company 2 0 2,5 5 7,5 10
  4. 4. Guess what? Number of months to reach 1 million usersCompany 1Company 2 0 2,5 5 7,5 10
  5. 5. Guess what? Number of months to reach 1 million usersCompany 1Company 2 0 2,5 5 7,5 10
  6. 6. Online learning and...flipping the classroom• Lectures at home• Homework in class• Let’s support this new types of classes!!!
  7. 7. Flipped session= computer lab session• Observations in lab sessions • Inefficient interactions • Problems orchestrating the session• Definition of metrics (ICALT’12) • Waiting time • Order
  8. 8. Proposed solution Data Process / Inter-collection Analysis ventions Attach infoEvents from Recommend to augmentedweb sensors interactions physical space
  9. 9. http://www.class-on.org• Awareness tool supporting teachers• Context • Lab sessions (f2f) = flipped session • Regulate interactions • Empower teacher for improving orchestration
  10. 10. BART SIMPSONLISA SIMPSON Student interface
  11. 11. BUSY2 35 2 Teacher interface
  12. 12. BUSYBART SIMPSON LISA SIMPSON Teacher interface
  13. 13. Orchestration aspectsthat could be improved• Based on the 5+3 aspects framework (ECTEL’12) • Management • Intervention • Assessment • Design
  14. 14. classON Evaluation• Quantitative data: not enough improvement• Teachers perception • info in a glance • fair distribution of feedback• Students perception • more fair (time/order) • trust the help is coming
  15. 15. classON Further Steps• Suggest peer support interactions• New strategies for feedback dispatching• Annotation system• Exploitation of qualitative data in questions & answers• New experiment(authoring tool): Superpowers to teachers (workshop at Madrid Science Week)• Visualization + gamification (explored in my stay at KUL, but in the context of Research 2.0)
  16. 16. Ambient displays• Not the main focus of • Design factors user attention • Appeal (usability +• Used for aesthetics) • Awareness • Learnability: match user’s • Persuade expectations• Contexts • Awareness: match user’s interests • Health / good habits • Distraction: low
  17. 17. Gamification• Applying game design and game mechanics to non-game contexts• Engage users in a new system• Engage users keep using the system• Metaphors for ambient displays using game concepts
  18. 18. GambientFication Ambient displays + Gamification (Powered by Analytics)• KU Leuven group• TiNYARM• Use iPads as ambient displays • Use during working hours • Use a holder as a photo frame• 10 last days of the stay
  19. 19. Evaluation• Awareness of research activity in the short/ medium/long term• Appeal (usability + aesthetics)• Distraction• User perception• Awareness/reflection/sense-making/ behavior change
  20. 20. And now, let’s jump to emotions...
  21. 21. Context ● Life-cycle of Learning Analytics process Data Process / Inter- Collection Analysis ventions Events from Virtual URLs Machine Bash commands Files Compilations13/09/2012 EEE Meeting 2012 1
  22. 22. Data collection ● Collect events from: – Web browser – Text editor – Programming tools – Session ● More information: – Partial grades – Forums13/09/2012 EEE Meeting 2012 2
  23. 23. Problem statement ● Detecting emotions in educational settings – Affective computing (Picard et al., 1997) – Use of physical sensors – Applied mainly in ITS ● Recommender systems in TEL – SoA Review (Draschler et al., 2011) – There are no implementations that consider the affective state of the learner13/09/2012 EEE Meeting 2012 3
  24. 24. Objectives ● Detect learner emotions from the events generated within a learning environment ● Recommend learning resources based on the detected emotions (and other variables) ● Reflecting affective states into the virtual environment ● Providing awareness of affective states13/09/2012 EEE Meeting 2012 4
  25. 25. Proposed solution ● Complete Learning Analytics cycle Data Process / Inter- Collection Analysis ventions Events from Detecting Recommend Virtual affective Learning Machine state Resources13/09/2012 EEE Meeting 2012 5
  26. 26. Classification of emotions ● Two prevalent ways: – Two-dimensional features (valence, arousal) – Discrete sets (e.g. Ekmans basic emotions: anger, disgust, fear, joy, sadness, surprise) ● Learning-domain set proposed by DMello et al. (2007): Boredom, confusion, frustration, eureka, motivation, neutral13/09/2012 EEE Meeting 2012 6
  27. 27. Processing events ● Input: Events collected during a working session ● Methods: Hidden Markov Models, GSP13/09/2012 EEE Meeting 2012 7
  28. 28. Intervention: Recommend resources ● Resource recommendation ● User-based (vs item) collaborative filtering ● Modified process: – Define preference model – Calculate user similarity including emotions – Select neighbors of learner – Obtain list of recommended items – Filter recommendations according to current emotion ● Implementation based on Apache Mahout13/09/2012 EEE Meeting 2012 8
  29. 29. Evaluation ● Affective state detection – Learner feedback – Instructor feedback – External sensors: ● Galvanic skin response ● Facial gestures ● Resource recommendation – Information system metrics: ● Precision and recall13/09/2012 EEE Meeting 2012 9
  30. 30. First Prototype ● Widget: Learning Resource Affective Recommender ● Among the winners of the 3rd ROLE Widget Enchantment ● Paper accepted in workshop MATEL 2012 ● Demo available!13/09/2012 EEE Meeting 2012 10
  31. 31. Future work ● Implementing two sensors for emotion: Galvanic skin response (Arduino-based), facial gestures (OpenCV-based) ● Analyzing the inclusion of other variables: – Grades, session duration, amount of events ● Experiments planned: – Detection: Madrids Science Week 2012 – Recommendation: C programming class ● Deployment on Amazon EC2 ● Integration with LearnGLASS13/09/2012 EEE Meeting 2012 11
  32. 32. Thank you! Questions?13/09/2012 EEE Meeting 2012 12

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