Affective User Modeling to Improve  Human-Computer Interaction Cristina Conati  Department of Computer Science University ...
Research Context (1) <ul><li>Adaptive User Interfaces   (AUI) </li></ul>AUI <ul><li>Fascinating interdisciplinary field ai...
Research Context (2) <ul><li>User Modeling :  how to efficiently infer, represent and reason about  non-trivial  user feat...
Long-term Research Goal <ul><li>Extend the range of features in a user model: from  cognitive elements  to  meta-cognitive...
Challenge <ul><li>Limited  information bandwidth  (amount and quality of  </li></ul><ul><li>information available to build...
How to Assess a User’s Emotions? <ul><li>Emotions can be assessed by </li></ul><ul><ul><li>Reasoning about possible causes...
Why do We Care? <ul><li>Assumption:  understanding user affect may enable an interface to better meet the user’s needs </l...
Outline <ul><li>Educational Computer Games </li></ul><ul><li>The Prime Climb Game </li></ul><ul><li>An Affective Student M...
Educational Games <ul><li>Educational systems designed to teach via game-like activities </li></ul><ul><li>Pros: generate ...
Example: The  Prime Climb Educational Game <ul><li>Designed by EGEMS group at UBC to teach number  </li></ul><ul><li>facto...
Our Solution <ul><li>Emotionally  Intelligent Pedagogical Agents that </li></ul><ul><li>Monitor how students learn from a ...
The Prime Climb Pedagogical Agent <ul><li>Provides hints to help students learn from the game </li></ul><ul><li>Hints base...
Challenge <ul><li>Difficulty of modeling affect enhanced by the fact that players often experience  </li></ul><ul><ul><li>...
Previous Approaches <ul><li>Reduce the uncertainty in modeling affect by </li></ul><ul><ul><li>Modeling one relevant  emot...
Our solution <ul><li>Handle the inherent uncertainty in modeling via  formal methods for probabilistic reasoning:  Bayesia...
The Prime Climb Affective Model   Player Reactions Predictive Assessment Emotional State Game-based Causes Based on the OC...
OCC Theory action OUTCOME Goals e.g., Have Fun Avoid Falling Defines 22 different emotions are the result of evaluating ( ...
The Prime Climb Affective Model   Player  Reactions Predictive Assessment Emotional State Game-based  Causes <ul><li>Infer...
Diagnistic Assessment <ul><li>We use an Electromiogram (EMG) sensor on the forehead to detects activity in the corrugator ...
The Prime Climb Affective Model   Player  Reactions Predictive Assessment Emotional State Game-based  Causes <ul><li>Infer...
Very Encouraging Results
Lots of Exciting Future Work <ul><li>Add more diagnostic elements to improve model accuracy (e.g., more expression recogni...
Conclusions <ul><li>Affective Computing has great potential to improve Human Computer interaction </li></ul><ul><li>Exciti...
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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 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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  • Modellazione affettiva sull’utente per migliorare l’interazione uomo-computer (Cristina Conati)

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

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