Pedagogical Agent Design for Distributed Collaborative Learning

2/12/2003 1

Pedagogical Agent Design for Distributed

Collaborative LearningWeiqin Chen and Anders Mørch*

Dept. of Information Science, University of Bergen

*InterMedia, University of Oslo

Half day tutorial at ICCE 2003, Hong Kong, December 2, 2003

ICCE 2003, HongKong 2/12/2003

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OutlineSoftware AgentsAgents in educationPedagogical agents in distributed collaborative learningA case study with demoLessons learned


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Software Agents


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OutlineDefinitionClassificationBrief historyResearch QuestionsTools and PlatformsAgent Applications


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What is an Agent? (1)An over-used term

autonomous agents, software agents, intelligent agents, interface agents, virtual agents, information agents, mobile agents

No commonly accepted notion


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What is an Agent (2)”computer programs that simulate a human relationship by doing something that another person could do for you”. T. Selker (1994)

”persistent software entity dedicated to a specific purpose”. D. smith, et al. (1994)

”situated in some environment, that is capable of flexible autonomous action in order to meet its design objectives”.

N. Jennings & M. Wooldridge (1998)

Other definitions in S.Franklin & A. Graesser (1997)

http://www.msci.memphis.edu/~franklin/AgentProg.html


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What is an Agent? (3)Weak Notion of Agency:

AutonomySocial abilityReactivityPro-activeness

Strong Notion of Agency:Belief, desire, intention


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What is an Agent? (4)Two extreme views:

Agents as essentially conscious, cognitive entities that have feelings, perceptions, and emotions just like humanAgents as automata and behave as they are designed and programmed


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Agent Taxonomy

Franklin and Graesser’s (1997) agent taxonomy


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Agent Typology

Typology based on Nwana’s (Nwana, 1996) primary attribute dimension


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Multiagent Systems (MAS)A loosely coupled network of problem solvers that

work together to solve problems that are beyond the individual capabilities or knowledge of each problem solver.

Characteristics:Limited viewpointNo global system controlDecentralized dataAsynchronous computation


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Origin of Software AgentsThe idea of an agent originated with John McCarthy in the mid-1950’s and the term was coined by Oliver G. Selfridge a few years later, when they were both at the Massachusetts Institute of Technology. They had in view a system that, when given a goal, could carry out the details of the appropriate computer operations and could ask for and receive advice, offered in human terms, when it was stuck. An agent would be a ”soft robot” living and doing its business within the computer’s world.

-Alan Kay

Computer Software, 1984


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Brief History(1)1994

CACM special issue on agents P. Maes, ”Agents that reduce work and information overload”D. Norman, et al. ”How might people interact with agents”

ATAL (Workshop on Agent Theories, Architectures, and Languages)

1995”Intelligent agents: Theory and Practice” by M. Wooldridge & N. Jennings ICMAS (Int. Conf. on Multi-Agent Systems)


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Brief History (2)1996

PAAM (Int. Conf. on Practical Application of Intelligent Agents and Multi-Agent Technology)FIPA (Foundation for Intelligent Physical Agents)

1997 J. M. Bradshaw, ”Software agents”AA (Int. Conf. on Autonomous Agents)

1998 JAMAS (Journal of Autonomous Multi-Agent Systems)

2002 AAMAS (joint of ICMAS, AA & ATAL)


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Agent-Enabled System Architecture (Bradshaw,

1997)


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Design Issues (1)Agent—User

Control -who takes control?

Understanding -how to make agents understandable /trustworthy?

Personification -how to present agent?

Distraction -how to minimize distraction?

User modelling -how to model emotion, intention, social behaviours, etc?

Privacy -how to protect privacy?


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Design Issues (2)Agent--Other Agents

How to find other agents?How to model other agents?How to communicate with other agents (language, ontology)?

KQML (Finn & Labrou, 1997) & KIF (Genesereth & Fikes, 1992), FIPA ACL & FIPA SLOntology (Gruber, 1993)

How to cooperate/negotiate with other agents?

AI techniques (inference, planning, logic, constraint satisfaction)


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Design Issues (3)Agent—Legacy softwareThree possible solutions (Genesereth &

Ketchpel, 1994)Rewriting the softwareTransducer (interpreter)Wrapper

Another suggestion:Web Services

Scalability, stability and performance


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Tools and PlatformsOOA (Open Agent Architecture) by SRI International’s AI Center

http://www.ai.sri.com/~oaa/

JATLite (Java Agent Template, Lite) by Center for Design Research, Stanford Univ.http://cdr.stanford.edu/ABE/JavaAgent.html

ZEUS by British Telecommunications (BT)http://more.btexact.com/projects/agents/zeus/index.htm

JADE (Java Agent Development Framework) by Telecom Lab, Italia (TILAB)http://sharon.cselt.it/projects/jade/


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Agent ApplicationsWorkflow managementNetwork managementAir-traffic controlBusiness process engineeringEntertainment

Personal assistantsE-mail filteringInformation managementData miningE-commerceEducation


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Agents in Education


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OutlineRoles of agents from CAI to CSCLSpectrum of educational systemsPositioning the agent componentSome examples

Intelligent tutoring systemsDomain-oriented design environmentsCollaborative learning environments


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Roles for agents: CAI to CSCL

Computer-AssistedInstruction

Computer Supported

Instructors

(Automated)Coaching

Intelligent TutoringSystems

Collaborative Learning

Problem-BasedLearning

Network ofColleaguesCritiquing

Systems

On-LineResources

(CD-ROM, WWW)

InstructorsOn-Line

FacilitatorsSuper-users

Micro-worlds

Learning on Demand


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Educational systems paradigms

Computer Aided Instruction (CAI)Intelligent Tutoring Systems (ITS)Microworlds (MW)Guided Discovery and Critiquing (GDC)Knowledge Building Environments (CSCL)

Note: the paradigms are continually evolving and mutually influencing each other


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Spectrum system-user control

Positioning educational systems along a line of increasing order of their enabling user control, or alternatively allowing predefined instructional sequencesOn the left: Instructional systems and ITSOne the right: open (”constructionist” and “constructivist”) learning environmentsIn between: Guided discovery and critiquing


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Distribution of (human-

computer) control in educational systems

CAI ITS GDC MW CSCL

On left: Systems supporting well-defined instructionOn right: Systems allowing user-defined interactionNote ! Comparison leaves out important variables ..


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Omission 1: number of users

First and second generation systems (CAI; ITS; GDC; MW) were primarily built for single usersThe field of CSCW had not yet maturedThird generation systems (CSCL) are multiuser, since the focus now is on how to support collaborative learning


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Omission 2: type of artefact

First generation systems (CAI, ITS) tended to favour behavioural and mental aspects of learning (psychology)Second generation systems (MW, GDC) put more emphasis on the physical aspects of learning (”learning by doing”)Third generation systems (CSCL) tend to favour conceptual aspects of learning (learning to reason)


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Positioning the agent component

Agents can support part of the “system functionality” of a learning environmentAgents can also support part of the “user work” in a learning environmentAgents are positioned somewhere in-between hard coded (programmed) functionality and informal rules to guide user interaction and social conduct


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1st Gen.: Tutors and Coaches

“Expert systems” for teaching and learningWorks best in well-defined domains (e.g. physics, computer programming)Instructional plannerHigh-level goals and strategiesIndividual student modelMany opportunities for agent (coach) interactionFew opportunities for “learning by discovery”


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Grace: A Tutor for Cobol


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Andes: ITS with coaching


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MicroworldsMicroworlds are not directly associated with agents, but have been important inspiration and platform for agent integrationMicroworlds define domain-specific “worlds” users can freely explore to build artefacts of the own creation and learn as a “by-product”Microworlds support constructionist learning or “learning by doing” (Harel & Papert, 1991)


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2nd gen.: Guided discovery

Combining open learning environments with teacher guidanceConceptual models and well-defined tasks to be discovered Learners construct knowledge themselves by being engaged; philosophy is “just try it”Teacher as facilitator “standing behind the shoulder” to encourage, challenge, and directGoal of teacher: stimulate students’ critical thinking skills


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2nd gen.: Critiquing systems

Computational approach to guided discovery by integrating Microworlds with ITS rule-basesConceptual foundation in Donald Schön’s theory of expert everyday knowledge building:

Learning by doing (“physical” activity)Learning by reflection (mental/conceptual aspect)

Application domainsLearning-on-demandDesign (architecture, network design, lunar habitat)

The computer-based critic systems are referred to as Integrated Design Environments


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Janus: The “doing” face


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Janus: The argumentation face


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Janus: CriticsCritics are intelligent interface agentsLinking “doing” and conceptualization, and correspondingly construction and argumentation“Breakdowns” in construction may create new (unanticipated) learning opportunitiesMaking students pause and reflectAbstract concepts are presented to students in a context when it is meaningful for them


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Comparing Tutors & CriticsTutors Critics

Individual adaptation

student model

user model

Instructional model

well-defined guided discovery

Problems well-defined “wicked”

Answers usually right/wrong

open-ended, argumentative

Multi-user no no


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CoPAS: Simulating CSCL Agents


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Pedagogical Agents in Distributed Collaborative Learning Environments


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General definitionPedagogical agent definition adopted from Johnson et al., (1999):

“Pedagogical agents can be autonomous and/or interface agents that support human learning in the context of an interactive learning environment.”


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3rd Gen.: Agents for CSCLAgents as online facilitators in CSCW and CSCL environmentsPedagogical agents operate in the context of collaboration systems, such as groupwareFacilitating communication and coordination among collaborating peersIn our case: facilitating knowledge building and progressive inquiry (to be discussed)


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CSCWComputer Supported Cooperative WorkCS-part focus on groupware, knowledge management & other collaboration systemsTechnical issues include: distribution, document sharing, coordination, awarenessCW-part address social aspects of using the systems by empirical (usually field) studiesAlso important are conceptual approaches, such as coordination theory and languages


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CSCLComputer Supported Collaborative LearningEducational CSCW applications for teaching and learning (school or workplace)Broad and multifaceted conceptual foundation, which includes :

Socio-cultural perspectivesSituated learningDistributed cognition


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Knowledge BuildingA technique for collaborative learningStudents learn by “talking” (reasoning aloud) for the purpose of developing explanations Formulating research questions, answering them independently, and finding support Structured as a discussion with message categories modelled after scientific discourseComputer supported by environments such as

CSILE and Knowledge ForumFle3


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Agents as facilitatorsMonitor participation in KB discussion and provide advice to the participantsEncourage non-active students to be more activeSuggest what messages to reply to and who should be doing soSuggest what category to choose for the next message to be postedSuggest when messages do not follow the scientific method of knowledge building, etc.


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Conceptual designAgents for CSCL can be designed from different perspectives:

Technological-based design Theory-based designEmpirical-based design

These perspectives can be combined


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Technological-based design

Build agent technology from Scratch (e.g. java, python)Existing agent development environments, such as JADE, ZEUS and Microsoft Agents

Build agent systems by integrating them with existing educational systems

Open source systems (e.g. Fle3)Other systems (e.g. Teamwave Workplace)

Combinations of the above is possible


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Theory-based design Coordination theoryConceptual models of collaborationPatterns of collaborative interaction, such as “genuine interdependence”:

sharing informationmindful engagementjoint construction of ideas


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Empirical-based designThis has been the main perspective in DoCTAExample of a finding that can drive design :Three kinds of postings in the forum

knowledge building propermeta commenting (discussion of the KB process)social talk and chatting

Design implicationshelp students with choosing KB categoriesoff-load some of meta commenting to agents


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Example of empirical data that have been used to

feed design

Table 2: One the left: Knowledge-building thread in FLE with notes coded according to Knowledge Building proper (KB), Meta

commenting (MC), and Social talk (ST). On the right: The postings of the pilot group. From Mørch, Dolonen & Omdahl (2003).


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Design space for pedagogical agents

Technological and conceptual dimensions providing guidance (questions, possibilities, constraints) for design:

presentationinterventiontaskpedagogy


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Presentation dimensionHow an agent should present itself to the userComputational technique: Separate window, overlapping window, pop-up box, animated character, etc.How to present information :Text, speech, graphics, body language simulation, etc.Examples (Office Assistant, STEVE, text pop-up in CoPAS)


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Intervention dimensionWhen an agent should present information to the user (a timing issue) Analogy with thermostat: When a certain environmental variable has reached a threshold value, an action is takenIntervention strategies to be decided:

degree of immediacy (how soon)degree of repetition (how often)degree of intrusiveness (block or superimpose)degree of eagerness (how important)


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Task dimensionInteracting with a learning environment w/agents is radically different from interaction with the same environment without agentsDifferent tasks may require different agents

Well-defined tasks (eg. physics) are different fromIll-defined tasks (e.g. city planning)

Agents can help to simplify the taskAgents can make the task harder to completeAgents can create breakdowns in task performance, causing problem restructuring


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Pedagogy dimension (CSCL)

Agents as conceptual awareness mechanism, coordinating multiple knowledge sources (humans and online resources)Serving as “missing link” in distributed settings

A new person just logged on need to be updatedInforming teachers about student activity

Agents embodying collaboration principlesDivision of labourParticipation and coordination of joint workAbout scientific discourse (knowledge building)


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Balancing the dimensionsMaking design decisions for agents based on choosing values for each of the four dimensions Do we need to take all of them into account in any one design, or is a subset sufficient? and are there other dimensions that could be included as well? Need also to balance human-agent distributionWe give example of the design decisions chosen for a specific project: DoCTA-NSS


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A Case Study with Demo


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OutlineBackground DoCTA-NSSPilot StudyFLE3Assistant in FLE3


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DoCTA-NSS DoCTA & DoCTA-NSS Design and use of Collaborative

Telelearning Artefacts – Natural Science Studios

Goal study social, cultural and pedagogical

aspects of artefacts in collaborative telelearning process and apply the findings to the design of environments

Pilot studygen-ethics scenario


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Kirsten’s(Instructor)Workspace

Sandgotna local group

user interface

Fle3Log

Subject MatterExpert

StudentCoach

EmailNotification

CurriculumManager

KB ActivityOverviewInstructor

Activity Organiizer

InstructorFle3 Agent

Fle3Server

Hovseter local group

user interface

InternetConnection

Mind-mapLog

Fle3

OnlineResources

Mindmap

DoCTA NSS: The physical setting

OnlineResources

Bergen

Mindmap

Fle3


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FLE3

Developed at UIAH Media Lab, University of Art and Design Helsinki

http://mlab.uiah.fi/

Builds on many years of research on networked learninghttp://www.helsinki.fi/science/networkedlearning/eng/index.html


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FLE

”Fle2 is design to support problem based learning (PBL) and inquiry learning. Fle2 helps students and teachers to engage in coordinated efforts to solve problems and build knowledge together”.

(Muukkonen, H; Hakkarainen K.; Leinonen T. 2000).


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Progressive Inquiry


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FLE3 Interface

From FLE3 website


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Agent Design IssuesFindings of pilot study

Students have difficulties in choosing categoriesInstructors have difficulties in following the collaboration and give advice

Facilitating Collaborative Knowledge Building

AwarenessPresenting advice and explanation

Learning from feedback


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FunctionalitiesAs an observer

Collect informationParticipant, activity, timestampLast log on, last contribution (for each participant)

Compute statisticsPresent statistics--chart

As an advisorPresent updates, statisticsAdvice instructor on possible problems and sending messages to studentsAdvice students on the use of categories


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Assistant in FLE3

FLE

logon/off(person, timestamp)

update in KB(msg, person, category, context, timestamp)

update in WebTop(person, content, timestamp)

response to advice (advice, delete/exlain/send)

aw areness (updates, statistics)

advice

monitoring

data

base

aw areness infogenerator(updates &statistics)

advice generator

advice analyzing

learning

rule

s

Facilitator Agent


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Student interface


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Instructor interface


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Technical DetailsDatabase

MySQL

Learning algorithmCN2 (Clark & Niblett, 1989)

Knowledge representationRuleML


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Demo

Http://nattergal.ifi.uib.no:8088/FLE


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Lessons LearnedScalability

from single user to multi user systemsfrom well defined to ill defined domains

CSCL needs more than (conceptual) knowledge building (what about domain-specific microworlds?)Instantiating various design dimensionsImportance of understanding collaborationIntegration of agents with human facilitationA full scale field study

Documents

Pedagogical Agent Design for Distributed Collaborative Learning