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An Affective Model Suitable to Infer the Student's Emotions in a Collaborative Learning Game

An Affective Model Suitable to Infer the Student's Emotions in a Collaborative Learning Game

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An Affective Model Suitable to Infer the Student's Emotions in a Collaborative Learning Game

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  1. An Affective Model Suitable to Infer the Student's Emotions in a Collaborative Learning Game Edilson Pontarolo (UTFPR, CAPES-COFECUB scholarship) Rosa M. Vicari (UFRGS) Patrícia A. Jaques Maillard (UNISINOS) Sylvie Pesty (INP Grenoble, sandwich)

  2. VALENCED REACTION TO VALENCED REACTION TO CONSEQUENCES OF EVENTS CONSEQUENCES OF EVENTS ACTIONS OF AGENTS ACTIONS OF AGENTS ASPECTS OF OBJECTS ASPECTS OF OBJECTS liking disliking etc. liking disliking etc. pleased displeased etc. pleased displeased etc. Approving disapproving etc. Approving disapproving etc. FOCUSING ON FOCUSING ON FOCUSING ON FOCUSING ON CONSEQUENCES FOR OTHER CONSEQUENCES FOR OTHER CONSEQUENCES FOR SELF CONSEQUENCES FOR SELF SELF AGENT SELF AGENT OTHER AGENT OTHER AGENT PROSPECTS IRRELEVANT PROSPECTS IRRELEVANT PROSPECTS RELEVANT PROSPECTS RELEVANT DESIRABLE FOR OTHER DESIRABLE FOR OTHER UNDESIRABLE FOR OTHER UNDESIRABLE FOR OTHER Happy for Resentment Happy for Resentment Gloating Pity Gloating Pity Pride Shame Pride Shame Admiration Reproach Admiration Reproach Joy Distress Joy Distress FORTUNES-OF-OTHERS FORTUNES-OF-OTHERS WELL-BEING WELL-BEING ATTRIBUTION ATRIBUTION Love Hate Love Hate Hope Fear Hope Fear ATRACTION ATRACTION CONFIRMED CONFIRMED DISCONFIRMED DISCONFIRMED Gratification Remorse Gratification Remorse Gratitude Anger Gratitude Anger Satisfaction Fear-confirmed Satisfaction Fear-confirmed Disappointment Releaf Disappointment Releaf WELL-BEING / ATRIBUTION COMPOUNDS WELL-BEING / ATTRIBUTION COMPOUNDS PROSPECT-BASED PROSPECT-BASED OCC Model social, moral and behavioral standards

  3. Big-Five Model Extroversion Agreeableness Conscientiousness Emotional Stability Openness to Experience

  4. v1 v2 v3 v4 v5 v6 Bayesian Network (BN) • Representation of Uncertain Knowledge (probabilities x beliefs) Pearl (1988, 1993, 2000) • Qualitative x Quantitative • Conditional Probability Tables .

  5. Collaborative Game j1_d1 j1_d2 Collaboration Collaboration Synchronous competition Shared problem Shared problem j2_d1 j2_d2

  6. Collaborative game Feedback

  7. Collaborative Game Collaboration Competition Protocol Socket TCP/IP Internet client server

  8. Personality Traits Goals Standards Partner’s actions User’s actions Atribution Emotions User Affective Model McCrae & Sutin (2007) Roberts & Robins (2000) Basic tendencies (traits)  Characteristic adaptation  Behavior tendencies Ortony, Clore & Collins (1988) Interaction Appraisal (behavioral standards)  Attribution emotions

  9. Results and Discussion Personality traits correlation CT = {x,y / x Є T, y Є T, T=1..4, x≠y} µT = ∑ | r x,y |  / 6 = 0,182 *Pearson's product-moment coefficient, r (-1 ≤ r ≤ +1)

  10. Results and Discussion Goals correlation CO = {i,j / i Є O, j Є O, O=1..5, i≠j} µO = ∑ | r i,j  |  / 10 = 0,234 r Beat_adversaries , Beat_partner= 0,485 Standards correlation CN=  {n,m / n Є N, m Є N, N=1..5, n≠m} µN = ∑ | r n,m | / 10 = 0,266 r Beat_user, Motivate_user= 0,473

  11. Results and Discussion Correlations: Traits x Goals CTO = {t,o / t Є T, o Є O, T=1..4, O=1..5} µTO = ∑ | r t,o | / 20 = 0,107 r Stability , Have_Fun= 0,340 Correlations: Traits x Standards CTN = {t,n/ t Є T, n Є N, T=1..4, N=1..5} µTN = ∑ | r t,n | / 20 = 0,142 r Extroversion , Standard_Motivate_User=0,320

  12. Results and Discussion Fisher’s Exact Test (FET) • pvalue (0 ≤ pvalue ≤ 1 ) , given: • - Fixed marginal totals • - Null hypothesis (A and B conditionally independent)

  13. Results and Discussion Two-tailed FET results: Traits x Goals

  14. Results and Discussion Two-tailed FET results: Traits x Standards

  15. Results and Discussion Quantitative Refinement: traits x goals, traits x standards 40 incomplete cases [ yes|no|null ] + 40 completed cases [ yes|no ] { t,o / t Є T, k Є O } + { t,n / t Є T, n Є N } Estimation-Maximization (EM) Algorithm Lauritzen (1995) Conditional Probability Tables P( Goals | Traits ) P( Standards | Traits )

  16. Results and Discussion Attribution emotions – user’s actions

  17. Results and Discussion Quantitative Refinement: emotions x user’s actions 351 incomplete cases [ yes|no|null ] + 351 completed cases [ yes|no ] {k,s / k Є (PAU N) , s=Proud  s=Shame} EM Algorithm Conditional Probability Tables P( Proud | Standards  user’s actions ) P( Shame | Standards  user’s actions )

  18. Results and Discussion Attribution emotions – partner’s actions

  19. Results and Discussion Quantitative Refinement: emotions x partner’s actions 351 incomplete cases [ yes|no|null ] + 351 completed cases [ yes|no ] {k,s / k Є (PPU N) , s=Admiration  s=Reproach} EM Algorithm Conditional Probability Tables P( Reproach | Standards  partner’s actions ) P( Admiration | Standards  partner’s actions )

  20. Future work • New learning experiments • Affective model implementation • Interaction patterns segmentation • New validation experiments • Dynamic BNs • More “pedagogical” collaborative games • Add an effective communication mechanism

  21. Thanks! epontarolo@utfpr.edu.br