4-th ieee international conference on advanced learning technologies, joensuu, finland, august 30...
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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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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
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
Kharkov National University of Radioelectronics (Ukraine)
Sergey Makovetskiy
Department of Software Engineering
Kharkov National University of Radioelectronics (Ukraine)
http://poaslab.kture.kharkov.ua
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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
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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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
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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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
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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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
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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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
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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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
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.