4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004 4-th IEEE International Conference

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4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 20044-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004

Personalized Distance LearningBased on

Multiagent Ontological System

Vagan Terziyan [email protected]

Igor Keleberda [email protected] Lesna [email protected] Sergey Makovetskiy [email protected]

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Authors

Vagan Terziyan

Industrial Ontologies Group

Department of Mathematical Information Technologies

University of Jyvaskyla (Finland)

http://www.cs.jyu.fi/ai/vagan

This presentation: http://www.cs.jyu.fi/ai/ICALT-2004.ppt

Igor Keleberda

Department of Software Engineering

Kharkov National University of Radioelectronics (Ukraine)

http://poaslab.kture.kharkov.ua

Natalya Lesna

Educational and Methodical Office


Sergey Makovetskiy

Department of Software Engineering


http://poaslab.kture.kharkov.ua

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Motivation (problem)

The majority of modern distant learning systems are characterized by usage of restricted set of educational materials.

On the other hand, they provide insufficient level of personalization of the learning process.

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Motivation (solution)

One possible way for overcoming mentioned difficulties is the usage of multiagent software technologies in the framework of the Semantic Semantic WebWeb activities of the W3С consortium.

These technologies are capable to automatically extract necessary educational materials (disposed over the whole Web space) to provide high-quality personalization of the education.

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What is Semantic Web ?What is Semantic Web ?

“The Semantic Web is a vision: the idea of having data on the Web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications”

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Semantic Web: New “Users”Semantic Web: New “Users”

SemanticAnnotations

Ontologies Logical Support

Languages Tools Applications /Services

Web content

UsersCreatorsWWWandBeyond

SemanticWeb

Semantic Webcontent

UsersSemanticWeb andBeyond

Creators applications

agents

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Semantic Web: What to Annotate ?Semantic Web: What to Annotate ?

Educational resources

Web resources / services / DBs / etc.

Web users (profiles,

preferences)

Web access devices

Industrial machines and devices

Web agents / applications

External world resources

Shared ontology

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IEEE Learning Technology Standards

1484.12.1: IEEE Standard for Learning Object Metadata (LOM)

1484.12.3:1484.12.3: Standard for XML binding for Learning Object Metadata data model

1484.12.4:1484.12.4: Standard for Resource Description Framework (RDF) binding for Learning Object Metadata data model

P1484.2.1/D8 Draft Standard for Learning Technology — Public and Private Information (PAPI) for Learners (PAPI Learner)

SWSW

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Semantic PersonalizationSemantic PersonalizationLearner

Agent-coordinator(semantic match engine)

Shared ontology

Shared ontology

Learning resource

Semantic annotation

Profile

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Global Understanding eNvironment (GUN)

GUN is an initiative of the Industrial Ontologies Group (IOG),

lead with the goal of extending the current Semantic Web to facilitate proactive, goal-driven, proactive, goal-driven,

self-maintainedself-maintained behavior of all kinds of resources that can be adapted to the Web.

http://www.cs.jyu.fi/ai/OntoGroup/

GUNGUN

ResourceResource

MetadataMetadata

Shared ontologyShared ontology

AgentAgent

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Agent’s Proactive Behavior in GUN (1)

GUNGUN

Able to make diagnostics of the learner and as result to know recent profile of the learner (learner’s state and condition);

Knows target profile (desirable state and condition according to e.g. curriculum);

Behaves to “maintain” the learner’s state (i.e. to minimize the gap between recent and target profiles);

Able to discover and utilize other resources and services to reach own goals .

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Agent’s Proactive Behavior in GUN (2)

GUNGUN

Able to check access rights to appropriate information;

Behaves to maximize the benefit for the commercial use of information from the resource;

Able to navigate external reader within the resource.

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From Web-Based Learning …

WWW

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… to GUN-Based Learning.

WWW

Semantic Web

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Mechanism of personalization

LOMLOMLOMLOM

LOMLOM

LOMLOM

LOMLOM

PAPIPAPILearnerLearner

PAPIPAPILearnerLearner

OOLL

OOLL

OOL L ,O,ORR

OORR OORROORR

OORR

OORR

OOL L ,, OORRSoftware agentSoftware agent MetadataMetadata OntologyOntology

Learning ResourceLearning ResourceLearnerLearner Agent Communication LanguageAgent Communication Language

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MOSPDL architecture

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MOSPDL algorithm

The MOSPDL algorithm contains the following stages:

user registers in the MOSPDL agent-coordinator sends query for educational

data profile learning resources agent creates the query to

educational resources in the Internet educational Internet-resources give metadata

for analysis of necessity of their usage in the learning process

cont…

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MOSPDL algorithm

agent-coordinator provides selection of educational materials; then it sends query for needed educational materials

learning resources agent builds the set of educational materials, which is recommended for the student

the agent-coordinator sends the resulting set to the personal agent; the personal agent produces multimedia learning output for the student

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The personal agent

The main task of the personal agent is creation of the user profile.

Algorithmic structure of the software agent contains the following stages:

the stage of registration the stage of learning

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The learning resources agent

The learning resources agent plays the role of a searching machine, which is capable to realize search on several resources simultaneously.

Algorithmic structure of the software agent contains the following stages:

the stage of forming of the profiles for educational materials

the stage of creation of the needed educational materials set

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The agent-coordinator

The agent-coordinator fulfils functions of the intermediary and realizes control over the learning process in the MOSPDL.

Algorithmic structure of the agent-coordinator contains the following stages:

the stage of searching for educational materials

the stage of individual selection of an educational material

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Distance learning portal

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Learning resource

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Conclusions

The designed software system belongs to a new generation of distributed systems of distant Web-based learning, namely to multiagent ontological systems based on Semantic Web.

The elaborated architecture and algorithm of MOSPDL is intended to solve the task of automation of the distant learning process, which is oriented on utilizing ontological models of student's profiles and learning resources profiles.

Documents

4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004 4-th IEEE International Conference