Distributed and United - Julita Vassileva
Transcript of Distributed and United - Julita Vassileva
J.Vassileva ICCE'2001 Invited Talk 1
Distributed and United
Julita VassilevaComputer Science DepartmentUniversity of SaskatchewanSaskatoon, Canada
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This talk is dedicated to my 9th grade math teacher
Emil Popov
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ContentsIndividualization Learning communitiesI-HelpThe issue of motivating participation
Through compassionThrough moneyThrough friends
Creating a culture
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What makes a good teacher?
Mastery of materialAbility to organize the presentation and
interaction with the learnerPedagogical 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 surprisePersonality and artistic skillCharisma …
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We have tried to simulate the teacher
CAI in the 60’sMultimedia courseware continues to be
“best-seller” in the 90’sWeb-based courses – big and growing
market now
Pedagogically – not too successful
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Focus on the individual
Dynamic Courseware Generation (DCG)Reactive Planning of:
ContentsPresentation strategyProblem-solving (coaching)
Overlay Student Modelling
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Dynamic Course Generation (DCG)(Vassileva, 1992, 1994, 1997)
Course
Plan
Student Model
0.850.3
0.9
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Pedagogical Planning in DCG
Ma ke Exerc ise
P resentEx ercise
Verify
Chec kRespo nse Inform Student
R emedy
Ex plainGiv e Correc t Solutio nElab orate onsub-p roblems Retry
GivHin
by des cription bya nalogy
Give Example Test Knowle dgePresent t he goal Show t ask planIntrodu ceE xplain
G ive prov oking Proble m
Give Exe rcise
Tea ch conce pt
Make Exercise
Present
Exercise
Verify
CheckResponse Inform
Student
Remedy
Explain
Give Correct Solution
Elaborate on
sub-problems
Retry
Give
Hint
by description by analogy
Give Example
Test KnowledgePresent the goal Show task plan
Introduce Explain
Give provoking
Problem
Give Exercise
Teach concept
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Coaching Users in Problem Solving
∫ + 221
xdx
∫ + 221
xdx∫ − 22
1xdx
duuu
∫ −421
2ln21
−x 2ln21
+x4ln
21
−u
4ln21 2 −x
Partial Fractions+
Substitution u=x2
Reverse Substitution
Algebraic Transformation
∫ − 42
221
x
dx
Variable under Differential
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Adaptive Teaching: Planning contents and delivery
Problem Solving
System teachingPlanning
contents
Planning delivery
User doing things
Initi
ativ
e
System
User
I too want individualized instruction!
DCG:
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The ultimate result?Instruction ultimately tailored to the
individual A system that truly “cares” for the learner
But maybe sometimes the learner should adapt, not the environment?Otherwise it would be a very lonely learner
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Dialectic between Cognition and Experience (Immanuel Kant, 1781).
Cognition**
Experience**Experience***
Experience****
Cognition* Cognition***
Cognition*
erience*
Experience*Maturana & Varella
Learning is adapting
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Learning is communication
Conversation Theory of Cognition (Pask, 1973)
Representation^
Representation^^
Representation^^^
Representation*
Representation**
Representation***
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Focus on the learning experience and communication
In the last 10 years we have seen:ConstructivismSocial cognitionCollaborative learning environmentsOpen learning environments
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Technology development
• distributed applications: plug-ins, applets, shared object libraries
• connected users, resources, applications• hybrid societies
Distributed Environments
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Learning communitiesHow to provide continuous support for
a community of learners?How to provide a shared medium?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!
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Large classesNo possibility for individualized
feedbackVarious levels of knowledge in the class
Students could help each other!(Jim’s and Gord’s idea)
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I-Help: a community of peers
?
Because..
…
But..
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What is the shared medium?
Public discussion forum (asynchronous)E-mail (asynchronous)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 Cognitive styleStar-signEagernessHelpfulnessSocial rankingRelationships
Modelling user’s knowledge
Modelling other individual characteristics
Modelling social characteristics of users in the context of the communityD
iffer
ent
Mat
chm
aker
s From self-evaluationFrom diagnostic applicationsFrom peer-helper evaluationFrom diagnostic application
From diagnostic applicationFrom peer-evaluation
From diagnostic applicationFrom self-evaluationFrom agent-evaluation
From self-evaluation
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Distributed learner modelling“Active”
No predefined behavior to be adapted Behavior Emerges !
“Just in time” generated models 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
Vassileva J., McCalla G.,Greer J., (to appear) Distributed UserModelling,in User Modelling andUser AdaptedInteraction, Special issue on Intelligent Agents.
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I-Help deployment results
Deployed for 2 years, 2000+ users, all undergrad CS classes, in the UK, France and Colombia
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
Greer J., McCalla G., Vassileva J., Deters R., Bull S., Kettel L. (2001) Lessons Learned in Deploying a Multi-Agent Learning Support System, ProceedingsAIED'2001,410-421.
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The most important lesson
The most exiting technology is worthless, if not embraced by a large user community
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)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
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Appealing to the compassionate
Animated, believable interface Agents Personal agent as a personaGoal:
to invoke emotion (compassion) in the userto persuade user to help
Study: the persuasive power of an “emotional” agent
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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
- 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”
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Emotional Engine in the PersonaValenced Reaction To
Consequences of Events (Pleased,
Displeased etc.)
Actions of Agents (Approving,
Disapproving etc.)
Aspects of Objects (Liking,
disliking etc.)
Emotion States
Happy Sad Pleased Surprised Neutral Angry
Facial expression for six major emotional states (Ortony,1988)
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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.
Preliminary Results
Girls felt a pressure to perform better in order to please the persona!
• all participants preferred the emotional persona
• no significant difference in the student’s performance
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Appealing to the materialistic
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The I-Help economy
Human help has costs (time, effort)! It shouldn’t be misused!
Market regulates the supply and demandhelp in exchange for currencyrate of pay negotiable (by agents)users can set parameters of agentspay a penalty if agent’s deals are ignored
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The agent negotiation
Each agent decides to counter-offer or accept an offer by calculating a utility function with factors:
Proceedings of ITSpringer LNCS 1839, 83-92
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
C. Mudgal, J. Vassileva (2000) Multi-agent negotiation to support an economy for online help andtutoring,
S'2000,
.
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The currency
A bit like Sun java’s “Duke Dollars”Everyone gets an initial allotment
helpees pay, helpers earnwhat happens when someone runs out?finding useful course resources pays
Redeemable for “prizes”
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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 usefulVisibility in the society based on reputation –Slashdot.com, thewines.com
In our caseMarks – not allowedSouvenirs not stimulatingReputation, visibility – a better way
…
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How to steer the economy?
Users
Resources
$
Pull money out by rewardingstudent with marks / items
$$$
Free instructional resources
$
$$
“Big Brother”
Users
Agent market economy
Allowance or “salary”
“Taxation”
Real world help market
Simulations:• Electronic marketplaces• Agent society, emerging norms• Emerging groups
Real world evaluations:• Real rewards• Monitoring user activity• Comparing with agent models• Reconstructing scenarios• Questionnaires
• Emerging cooperation among agents• Ethical vs. non-ethical agents• Looking for equilibrium states• What measures effect the emerging behaviour and equilibriums
social systems simulation
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Friends Forever
Supporting social relationships
Agents can take into account interpersonal relationships
In seeking helpIn negotiation
Agents can help in building new relationships among users
Agent coalition formationTrust-based mechanism
Forming user groups
Breban S., Vassileva J.(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 societyincreased 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?
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Create a learning culture!
The 5 “R”s (Hilarie Davis): Roles – people who sustain the communityRules – ethics of behaviour, reward systemRituals – 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!”
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How to ensure focus and guidance?
Rituals – How to make users feel safe:“Anonymous” usersOne user different rolesBut then how do you find yournew I-Help friend in the real classroom?
?
How to plan surprise?
How to make them come back again?
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Conclusions
Ultimate Individualization Distributed, Lonely Learners
Learning Communities United Learners
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ConclusionsSupporting Learner Communities
Multi-Agent Architectures provide a lot of useful tools and metaphorsIndividualization is important 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!
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Questions?
More Information: http://julita.usask.ca/http://bistrica.usask.ca/madmuc/http://www.cs.usask.ca/projects/aries
Contact: [email protected]