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)

01/06/2009

OCC Model VALENCED REACTION TO

CONSEQUENCESOF

EVENTS

pleaseddispleased

etc.

FOCUSING ON

CONSEQUENCES FOR OTHER

DESIRABLEFOR OTHER

Happy forResentment

UNDESIRABLEFOR OTHER

CONSEQUENCESFOR SELF

FORTUNES-OF-OTHERS

GloatingPity

PROSPECTS IRRELEVANT

HopeFear

ACTIONSOF

AGENTS

ASPECTSOF

OBJECTS

Approvingdisapproving

etc.

likingdisliking

etc.

FOCUSING ON

SELF AGENT

OTHERAGENT

PROSPECTSRELEVANT

JoyDistress

WELL-BEING

PrideShame

ATRIBUTION

AdmirationReproach

LoveHate

ATRACTION

DISCONFIRMEDCONFIRMED

SatisfactionFear-confirmed

DisappointmentReleaf

PROSPECT-BASEDWELL-BEING / ATRIBUTION

COMPOUNDS

GratificationRemorse

GratitudeAnger

social, moral and behavioral standards

VALENCED REACTION TO

CONSEQUENCESOF

EVENTS

pleaseddispleased

etc.

FOCUSING ON

CONSEQUENCES FOR OTHER

DESIRABLEFOR OTHER

Happy forResentment

UNDESIRABLEFOR OTHER

CONSEQUENCESFOR SELF

FORTUNES-OF-OTHERS

GloatingPity

PROSPECTS IRRELEVANT

HopeFear

ACTIONSOF

AGENTS

ASPECTSOF

OBJECTS

Approvingdisapproving

etc.

likingdisliking

etc.

FOCUSING ON

SELF AGENT

OTHERAGENT

PROSPECTSRELEVANT

JoyDistress

WELL-BEING

PrideShame

ATTRIBUTION

AdmirationReproach

LoveHate

ATRACTION

DISCONFIRMEDCONFIRMED

SatisfactionFear-confirmed

DisappointmentReleaf

PROSPECT-BASEDWELL-BEING / ATTRIBUTION

COMPOUNDS

GratificationRemorse

GratitudeAnger

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Big-Five Model

Extroversion

Agreeableness

Emotional Stability

Conscientiousness

Openness to Experience

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Bayesian Network (BN)

Representation of Uncertain Knowledge (probabilities x beliefs)Pearl (1988, 1993, 2000)

Qualitative x Quantitative Conditional Probability Tables.

v1

v2

v3

v4

v6

v5

v5 Yes No

v6 Yes ? ?

No ? ?

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Collaborative Game

Col

labo

ratio

n

j2_d2

j1_d2

j1_d1

j2_d1

Col

labo

ratio

n

Synchronouscompetition

Shared problem Shared problem

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Collaborative game

FeedbackFeedback

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Collaborative GameDENY_NICK <Nick_Name>

ACCEPT_NICK <Nick_Name>

PARTNERS <Ø> | <List_of_Available_Partners>

WAIT <Partner> <Nick_Name>

COMPETITION <Sudoku_Problem> <Sudoku_Solution> <Partner> <Nick_Name> <Advers1>

<Advers2>

PUT <Nick_Name> <Number> <Row> <Col>

PROPOSE <Nick_Name> ERASE | REPLACE<Number> <Row> <Col>

[<Justification>]

AGREE <Nick_Name> ERASE | REPLACE <Number> <Row> <Col>

DISAGREE <Nick_Name> ERASE | REPLACE <Number> <Row> <Col>

[ Justification ]

DOWN_PARTNER

NICK <Nick_Name>

INVITATION <Nick_Name> <Partner>

DENY <Partner> <Nick_Name>

ACCEPT <Partner> <Nick_Name>

PUT <Nick_Name> <Number> <Row> <Col>

PROPOSE <Nick_Name> ERASE | REPLACE <Number> <Row> <Col> [ <Justification> ]

AGREE <Nick_Name> ERASE | REPLACE <Number> <Row> <Col>

DISAGREE <Nick_Name> ERASE | REPLACE <Number> <Row> <Col>

<Justification>]

MESSAGE <Nick_Name> <Partner> <Message>

Socket TCP/IP

CollaborationCompetition

Protocol

Internet

client server

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User Affective Model

PersonalityTraits

User’s actions

Standards Goals

Atribution Emotions

Partner’s actions

McCrae & Sutin (2007)Roberts & Robins (2000)

Basic tendencies (traits)

Characteristic adaptation

Behavior tendencies

Ortony, Clore & Collins (1988)

Interaction Appraisal(behavioral standards)

Attribution emotions

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Results and Discussion

Trait Trait

Extroversion Agreeableness Conscientiousness Stability

Extroversion 1,000 0,052 -0,146 -0,068

Agreeableness 0,052 1,000 0,222 0,391

Conscientiousness -0,146 0,222 1,000 0,219

Stability -0,068 0,391 0,219 1,000

*Pearson's product-moment coefficient, r (-1 ≤ r ≤ +1)

Personality traits correlation

CT = {x,y / x Є T, y Є T, T=1..4, x≠y} µT = ∑ | r x,y | / 6 = 0,182

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Results and Discussion

Goals correlationCO = {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 correlationCN = {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

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Results and Discussion

Correlations: Traits x GoalsCTO = {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 StandardsCTN = {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

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Results and Discussion

Fisher’s Exact Test (FET)

pvalue (0 ≤ pvalue ≤ 1 ) , given:- Fixed marginal totals- Null hypothesis (A and B conditionally independent)

B=yes B=no

A=yes a b a+b

A=no c d c+d

a+c b+d

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Results and Discussion

Two-tailed FET results: Traits x Goals

TraitGoal

Extroversion Agreeableness Conscientiousness Stability

Beat_adversaries 1,00 0,02 0,19 0,66Beat_partner 0,20 1,00 1,00 0,33

Motivate_partner 0,41 0,25 0,23 1,00Have_fun 0,08 1,00 1,00 0,19

Negociate_a_solution 0,65 1,00 0,35 0,35

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Results and Discussion

Two-tailed FET results: Traits x Standards

TraitStandard

Extroversion Agreeableness Conscientiousness Stability

Beat_adversaries 1,00 0,24 0,22 0,49Beat_partner 0,72 0,17 0,47 0,28

Motivate_partner 0,13 0,29 0,46 0,46Have_fun 0,69 1,00 0,23 1,00

Negociate_a_solution 0,39 0,11 0,19 1,00

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Results and Discussion

Quantitative Refinement: traits x goals, traits x standards

40 incomplete cases [ yes|no|null ]+

40 completed cases [ yes|no ]

Estimation-Maximization (EM) Algorithm

{ t,o / t Є T, k Є O } +

{ t,n / t Є T, n Є N }

Conditional Probability TablesP( Goals | Traits )

P( Standards | Traits )

Lauritzen (1995)

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Results and Discussion

Attribution emotions – user’s actions

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Results and Discussion

Quantitative Refinement: emotions x user’s actions

351 incomplete cases [ yes|no|null ]+

351 completed cases [ yes|no ]

EM Algorithm

{k,s / k Є (PA U N) , s=Proud s=Shame}

Conditional Probability TablesP( Proud | Standards user’s actions )P( Shame | Standards user’s actions )

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Results and Discussion

Attribution emotions – partner’s actions

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Results and Discussion

Quantitative Refinement: emotions x partner’s actions

351 incomplete cases [ yes|no|null ]+

351 completed cases [ yes|no ]

EM Algorithm

{k,s / k Є (PP U N) , s=Admiration s=Reproach}

Conditional Probability TablesP( Reproach | Standards partner’s actions )P( Admiration | Standards partner’s actions )

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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

01/06/2009

Thanks!

epontarolo@utfpr.edu.br