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Modellazione affettiva sull’utente per migliorare l’interazione uomo-computer (Cristina Conati)
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Modellazione affettiva sull’utente per migliorare l’interazione uomo-computer (Cristina Conati)

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Women&Technologies: Research and Innovation. Nell'ambito del prestigioso WCC, (World Computer Congress), una conferenza nella conferenza dedicata alle donne e alle tecnologie, con un particolare......

Women&Technologies: Research and Innovation. Nell'ambito del prestigioso WCC, (World Computer Congress), una conferenza nella conferenza dedicata alle donne e alle tecnologie, con un particolare focus su ricerca e innovazione. Presentazione per l'intervento a distanza di Cristina Conati (University of British Columbia, Vancouver), intitolato "Modellazione affettiva sull’utente per migliorare l’interazione uomo-computer".

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  • 1. Affective User Modeling to Improve Human-Computer Interaction Cristina Conati Department of Computer Science University of British Columbia
  • 2. Research Context (1)
    • Adaptive User Interfaces (AUI)
    AUI
    • Fascinating interdisciplinary field aiming to
      • Create user interfaces that can better support individual users
      • By autonomously and intelligently adapting to their specific needs
    Cognitive Science HCI AI
  • 3. Research Context (2)
    • User Modeling : how to efficiently infer, represent and reason about non-trivial user features relevant for adaptivity .
    User Model Adaptation Inference Representation Inference
  • 4. Long-term Research Goal
    • Extend the range of features in a user model: from cognitive elements to meta-cognitive abilities and affective states .
      • Affective States
      • Emotions
      • Moods
      • Motivation…
    From Adaptation Cognitive Elements User Model
      • Meta Cognitive Abilities
      • Learning from examples
      • Reasoning by analogy
      • Self-monitoring…
    • Cognitive
    • elements
    • Knowledge
    • Goals,
    • Beliefs…
    User Model
  • 5. Challenge
    • Limited information bandwidth (amount and quality of
    • information available to build the model)
    • It can be difficult to unobtrusively capture the relevant traits from simple interaction events
    • High level of uncertainty
  • 6. How to Assess a User’s Emotions?
    • Emotions can be assessed by
      • Reasoning about possible causes (i.e. the interface keeps interrupting the user, so she is probably frustrated)
      • Looking at the one’s reactions (i.e. the user punches the screen, so she is probably frustrated)
    • However, the mapping between emotions, their causes and their effects can be highly ambiguous
    • Very hard to build models of user affect
  • 7. Why do We Care?
    • Assumption: understanding user affect may enable an interface to better meet the user’s needs
    • Especially in emotionally-charged context such as
      • E-health
      • E-games
      • Computer-based education
    • We have been working on affective user modeling for an educational computer game
  • 8. Outline
    • Educational Computer Games
    • The Prime Climb Game
    • An Affective Student Model for Prime Climb
    • Future work and Conclusions
  • 9. Educational Games
    • Educational systems designed to teach via game-like activities
    • Pros: generate high level of emotional engagement and motivation.
    • Cons:
      • Often possible to play the game without understanding the underlying knowledge
      • Suitable only for certain types of learners
  • 10. Example: The Prime Climb Educational Game
    • Designed by EGEMS group at UBC to teach number
    • factorization to students in 6 th and 7 th grade (11 and 12 year old)
  • 11. Our Solution
    • Emotionally Intelligent Pedagogical Agents that
    • Monitor how students learn from a game
    • Generate tailored interventions to trigger constructive reasoning…
    • … while maintaining a high level of student emotional engagement
    Crucial to model student affect in addition to learning
  • 12. The Prime Climb Pedagogical Agent
    • Provides hints to help students learn from the game
    • Hints based on
      • A model of student learning (Manske and Conati 2005) - for now
      • AND a model of student affect – in the future
  • 13. Challenge
    • Difficulty of modeling affect enhanced by the fact that players often experience
      • Multiple emotions
      • Possibly overlapping
      • Rapidly changing
    • For instance:
      • Happy with a successful move but upset with the agent who tells them to reflect about it
      • Ashamed immediately after because of a bad fall
  • 14. Previous Approaches
    • Reduce the uncertainty in modeling affect by
      • Modeling one relevant emotion in a restricted situation [e.g., Healy and Picard, 2000; Hudlicka and McNeese, 2002]
      • Modeling only intensity and valence of emotional arousal [e.g., Ball and Breeze, 2000]
    • Not ideal for precise affective interventions in the complex emotional context triggered by edu-games
  • 15. Our solution
    • Handle the inherent uncertainty in modeling via formal methods for probabilistic reasoning: Bayesian networks and their extensions
    • Integrate information on both causes and effects of emotional reaction
    • Based model design on existing, well-established theories from emotional psychology
  • 16. The Prime Climb Affective Model Player Reactions Predictive Assessment Emotional State Game-based Causes Based on the OCC Theory of Emotions (Ortony Clore and Collins, 1998) Diagnistic Assessment
  • 17. OCC Theory action OUTCOME Goals e.g., Have Fun Avoid Falling Defines 22 different emotions are the result of evaluating ( appraising ) the current circumstances with respect of one’s goals Joy/Distress Admiration/Reproach Pride/Shame Joy/Distress action OUTCOME Goals
  • 18. The Prime Climb Affective Model Player Reactions Predictive Assessment Emotional State Game-based Causes
    • Infers player goals at runtime (e.g., Have Fun, Learn Math, Avoid Falling…)
    • Has information to assess which game states satisfy/dissatisfy the goals
    Diagnistic Assessment
      • 6 of the 22 emotions in the OCC theory
      • Joy/Regret toward the game
      • Admiration/Reproach toward the agent
      • Pride/Shame toward oneself
  • 19. Diagnistic Assessment
    • We use an Electromiogram (EMG) sensor on the forehead to detects activity in the corrugator muscle
    • Previous studies (e.g., Cacioppo 1993) show that
      • greater muscle activity is a reliable indicator of negative affect
      • reduced activity is an indicator of positive affect
  • 20. The Prime Climb Affective Model Player Reactions Predictive Assessment Emotional State Game-based Causes
    • Infers player goals at runtime (e.g., Have Fun, Learn Math, Avoid Falling…)
    • Has information to assess which game states satisfy/dissatisfy the goals
    Diagnistic Assessment
      • Includes 6 of the 22 emotions in the OCC theory
      • Joy/Regret toward the game
      • Admiration/Reproach toward the agent
      • Pride/Shame toward oneself
      • EMG to detect corrugator muscle activity
      • Helps the model understand if the player is feeling a positive or negative activity
  • 21. Very Encouraging Results
  • 22. Lots of Exciting Future Work
    • Add more diagnostic elements to improve model accuracy (e.g., more expression recognition, speech/intonation patterns)
    • Integrate model of affect and model of learning
    • Create emotionally intelligent agent that takes into account both student affect and learning to decide how to act
    • Prove that it works better than agent with no affect!
  • 23. Conclusions
    • Affective Computing has great potential to improve Human Computer interaction
    • Exciting multi-disciplinary field that brings computer science together with disciplines traditionally more appealing to women (Cognitive Science, Psychology)
    • Not sure if this means that women may be privileged in the developments and use of the next generation ICTs
    • But I have been privileged to be working in this field and I really hope that more people will join