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