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Distributed and United

J.Vassileva ICCE'2001 Invited Talk 1

Julita VassilevaComputer Science DepartmentUniversity of SaskatchewanSaskatoon, Canada

This talk is dedicated to my 9th grade math teacher

E il P

J.Vassileva ICCE'2001 Invited Talk 2

Emil Popov

ContentsIndividualization Learning communitiesI-HelpTh i f ti ti ti i ti

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The issue of motivating participationThrough compassionThrough moneyThrough friends

Creating a culture

What makes a good teacher?

Mastery of materialAbility to organize the presentation and interaction with the learner

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te act o t t e ea ePedagogical skillsUnderstanding the learner

These features have been addressedby research in AI and Education

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What makes a great teacher?

Ability to guide “discovery”, leadershipAbility to motivate and pushSpontaneity and surprise

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Spontaneity and surprisePersonality and artistic skillCharisma …

We have tried to simulate the teacher

CAI in the 60’sMultimedia courseware continues to be “b t ll ” i th 90’

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“best-seller” in the 90’sWeb-based courses – big and growing market now

Pedagogically – not too successful

Focus on the individual

Dynamic Courseware Generation (DCG)Reactive Planning of:

Contents

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ContentsPresentation strategyProblem-solving (coaching)

Overlay Student Modelling

Dynamic Course Generation (DCG)(Vassileva, 1992, 1994, 1997)

Course

Plan

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Course

Student Model

0.850.3

0.9

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Pedagogical Planning in DCG

Give Example Test KnowlePresent the goalShow task planIntrod uceExplainProble m Give Ex ercise

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Ma ke ExercisePresentExercise VerifyChec kRespo nseInform Student RemedyE xplainGive Correct SolutiElaborate onsub-problemby description by analogy

Make ExercisePresentExerciseVerifyCheckResponseInform StudentRemedyExplainGive Correct SolutionElaborate onsub-problemsRetryGiveHintby descriptionby analogyGive ExampleTest KnowledgePresent the goalShow task planIntroduceExplainGive provoking ProblemGive ExerciseTeach concept

Coaching Users in Problem Solving

∫ + 221

xdx

∫ + 221

xdx∫ − 22

1xdx

Partial Fractions+

Substitution∫ − 42

221

x

dx

Variable under Differential

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∫ + 22 x2

duuu

∫ −421

2ln21

−x 2ln21

+x4ln

21

−u

4ln21 2 −x

Substitution u=x2

Reverse Substitution

Algebraic Transformation

Adaptive Teaching: Planning contents and delivery

Planning

System

DCG:

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Problem Solving

System teachingPlanning

contents

Planning delivery

User doing things

Initi

ativ

e

User

I too want individualized instruction!

The ultimate result?Instruction ultimately tailored to the individual A system that truly “cares” for the learner

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But maybe sometimes the learner should adapt, not the environment?Otherwise it would be a very lonely learner

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Learning is adapting

Dialectic between Cognition and Experience (Immanuel Kant, 1781).

Cognition**

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Cognition

Experience**Experience***

Experience****

Cognition* Cognition***

Cognition*

erience*

Experience*Maturana & Varella

Learning is communication

Conversation Theory of Cognition (Pask, 1973)

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Representation* Representation^

Representation** Representation^^

Representation^^^Representation***

Focus on the learning experience and communication

In the last 10 years we have seen:ConstructivismSocial cognition

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Social cognitionCollaborative learning environmentsOpen learning environments

Technology development

• distributed applications: plug-ins, applets, shared object libraries

connected users resources applications

Distributed Environments

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• connected users, resources, applications• hybrid societies

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Learning communitiesHow to provide continuous support for a community of learners?How to provide a shared medium?

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How to cater to the individual ? How to motivate participation?How to ensure focus and guidance?

These are some of the features that make the great teacher!

Large classesNo possibility for individualized

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p yfeedbackVarious levels of knowledge in the class

Students could help each other!(Jim’s and Gord’s idea)

I-Help: a community of peersBecause..

…

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?But..

What is the shared medium?

Public discussion forum (asynchronous)E-mail (asynchronous)

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( y )2-line chat tool (synchronous) Chat-rooms (synchronous)

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Individualization in I-Help

In matching people with appropriate partners depending on their

Knowledge l

Modelling user’s knowledge

aker

s From self-evaluationFrom diagnostic applicationsF di ti li ti

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Cognitive styleStar-signEagernessHelpfulnessSocial rankingRelationships

Modelling other individual characteristics

Modelling social characteristics of users in the context of the communityD

iffer

ent

Mat

chm

a From diagnostic applicationsFrom peer-helper evaluationFrom diagnostic application

From diagnostic applicationFrom peer-evaluation

From diagnostic applicationFrom self-evaluationFrom agent-evaluation

From self-evaluation

Distributed learner modelling “Active”

No predefined behavior to be adapted Behavior Emerges !

“Just in time” generated models D di

Vassileva J., McCalla G.,Greer J., (to appear) Distributed UserModelling,in User Modelling andUser AdaptedInteraction

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Depending on:The purpose of adaptationThe available user model informationThe trust relationships with other agentsThe circumstances (who is around) and resources

Focus on the process, not representation

Interaction, Special issue on Intelligent Agents.

I-Help deployment results

Deployed for 2 years, 2000+ users, all undergrad CS classes, in the UK, France and Colombia

Lessons learned:

Greer J., McCalla G., Vassileva J., Deters R., Bull S., Kettel L. (2001)

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Lessons learned: Usage / participation varies greatlyShould be perceived as adding value

Initial knowledge investment from instructor is crucial (apprenticeship)After reaching a “critical mass” becomes self-feeding

Kettel L. (2001) Lessons Learned in Deploying a Multi-Agent Learning Support System, ProceedingsAIED'2001,410-421.

The most important lesson

The most exiting technology is worthless, if not embraced by a large user community

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Example: NAPSTER, KaZaA

A very simple technology can be invaluable, if supported by an active user community

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How to motivate participation?

Why do people offer their time and resources? Different people have different motivationsSome are altruists (intrinsically motivated)S ld h l th i f i d d h t k

…

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Some would help their friends and hope to make new friends through helping Some seek glorySome seek attentionSome seek high marks Some seek money…

(extrinsically motivated)

Friends Forever

We need to provide a mechanism that appeals toevery individual, depending on his/her motivation

Animated, believable interface Agents Personal agent as a personaGoal:

Appealing to the compassionate

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Goal: to invoke emotion (compassion) in the userto persuade user to help

Study: the persuasive power of an “emotional” agent

Experiment with EmotionGoal: to find if integrating emotional qualities into personasimpacts the student’s performance and perception of learning

• Experiment:An introductory interactive course on C++ delivered by an animated persona

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- material presented by human voice

- users have to answer test questions

- persona responds to test performance with facial expression

• Two test conditions:- Emotional engine = “on”- Emotional engine = “off”

Emotional Engine in the PersonaValenced Reaction To

Consequences of Events (Pleased,

Actions of Agents (Approving,

Disapproving etc.)

Aspects of Objects (Liking,

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Displeased etc.) disliking etc.)

Emotion States

Happy Sad Pleased Surprised Neutral Angry

Facial expression for six major emotional states (Ortony,1988)

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Preliminary Results

Girls felt a pressure to perform better in order to please the persona!all participants preferred the

Okonkwo, C., Vassileva, J. (2001) Affective Pedagogical Agents and User Persuasion, C. Stephanidis (ed.) Proc. "Universal Access in Human-Computer Interaction (UAHCI)", HCI International,New Orleans 397-401

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• all participants preferred the emotional persona

• no significant difference in the student’s performance

New Orleans,397 401.

Appealing to the materialistic

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Human help has costs (time, effort)! It shouldn’t be misused!

The I-Help economy

Market regulates the supply and demand

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help in exchange for currencyrate of pay negotiable (by agents)users can set parameters of agentspay a penalty if agent’s deals are ignored

The agent negotiation

Each agent decides to counter-offer or accept an offer by calculating a utility function with factors:

i t ( di ti i f )

C. Mudgal, J. Vassileva (2000) Multi-agent negotiation to support an economy for online help andtutoring, Proceedings of ITS'2000, Springer LNCS 1839, 83-92.

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money importance (greediness, stinginess of user)importance of the current goalimportance of the relationship between the usersrisk attitudeperceived utility function and factors of the other agent agents model each other

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The currency

A bit like Sun java’s “Duke Dollars”Everyone gets an initial allotment

helpees pay, helpers earn

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helpees pay, helpers earnwhat happens when someone runs out?finding useful course resources pays

Redeemable for “prizes”

How to cash in the end?Depends on the values of the community

Marks – in a real classroom settingReal money – in a workplace setting /distance educReputation (top 10 list) – always useful

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Visibility in the society based on reputation –Slashdot.com, thewines.com

In our caseMarks – not allowedSouvenirs not stimulatingReputation, visibility – a better way

…

How to steer the economy?

Resources

Pull money out by rewardingstudent with marks / items

$$$

Free instructional resources

$

$$

“Big Brother”

Agent market economy

Allowance or “salary”

“Taxation”Simulations:• Electronic marketplaces• Agent society, emerging norms• Emerging groups • Emerging cooperation among agents

Ethi l thi l t

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Users

$

Users

Agent market economy

Real world help market

Real world evaluations:• Real rewards• Monitoring user activity• Comparing with agent models• Reconstructing scenarios• Questionnaires

• Ethical vs. non-ethical agents• Looking for equilibrium states• What measures effect the emerging behaviour and equilibriums

social systems simulation

Friends Forever

Supporting social relationships

Agents can take into account interpersonal relationships

In seeking helpIn negotiation

Breban S., Vassileva J.(2001)

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In negotiationAgents can help in building new relationships among users

Agent coalition formationTrust-based mechanism

Forming user groups

(2001)Long-Term Coalitions for the ElectronicMarketplace,Proc.E-commerceWorkshop,Canadian AIConference, Ottawa.

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Simulation results

In the agent world, simulation shows that coalition formation brings

stability, predictability in the agent societyi d b fit f th t

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increased benefits for the agents

In the human world, still to evaluate Can people become friends through their agents?Does agent coalition help build stable and productive learner teams?

Create a learning culture!

The 5 “R”s (Hilarie Davis): Roles – people who sustain the communityRules – ethics of behaviour, reward system

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Rituals – routines, predictable things, to make the participants feel safeRounds – regular events, customs, things to expect and look forward toRings – surprises, interesting unexpected things, “be always there or you’ll miss it!”

How to ensure focus and guidance?

Rituals – How to make users feel safe:“Anonymous” usersOne user different rolesBut then how do you find your

?

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But then how do you find yournew I-Help friend in the real classroom?

How to plan surprise?

How to make them come back again?

Conclusions

Ultimate Individualization Distributed, Lonely Learners

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Learning Communities United Learners

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ConclusionsSupporting Learner Communities

Multi-Agent Architectures provide a lot of useful tools and metaphorsIndividualization is important

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Motivation is important (individualization here too!)Research into heterogeneous agent economiesis needed (differently motivated agents)Studying the culture of the user population and creating a productive learning culture is vital!

Questions?

More Information: http://julita.usask.ca/http://bistrica usask ca/madmuc/

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http://bistrica.usask.ca/madmuc/http://www.cs.usask.ca/projects/aries

Contact: jiv@cs.usask.ca