Personalized Distance Learning
Based on
Multiagent Ontological System
1
Vagan Terziyan
Igor Keleberda
Natalya Lesna
Sergey Makovetskiy
vagan@it.jyu.fi
I.Keleberda@ieee.org
lesna@kture.kharkov.ua
sdmakovetskiy@ukr.net
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Authors
2
Igor Keleberda
Sergey Makovetskiy
Department of Software
Engineering
Department of Software
Engineering
Kharkov National University of
Radioelectronics (Ukraine)
Kharkov National University of
Radioelectronics (Ukraine)
http://poaslab.kture.kharkov.ua
http://poaslab.kture.kharkov.ua
Natalya Lesna
Vagan Terziyan
Educational and Methodical Office
Industrial Ontologies Group
Kharkov National University of
Radioelectronics (Ukraine)
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
4-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.
3
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Motivation (solution)
4
One possible way for overcoming mentioned
difficulties is the usage of multiagent software
technologies in the framework of the Semantic
Web 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.
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
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
5 applications”
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Semantic Web: New “Users”
Semantic
Web and
Beyond
Users
Creators
applications
Semantic Web
content
agents
Semantic
Annotations
Ontologies
Logical Support
Languages
Tools
Applications /
Services
Semantic
Web
6 WWW
and
Beyond
Creators
Users
Web content
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Semantic Web: What to Annotate ?
Industrial
machines
and devices
Web resources /
services / DBs / etc.
Web users
(profiles,
preferences)
Shared
ontology
7
Web
access
devices
External world
resources
Web agents /
applications
Educational
resources
4-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
SW
8
Object Metadata (LOM)
 1484.12.3: Standard for XML binding for
Learning Object Metadata data model
 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)
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Semantic Personalization
Learner
Shared
ontology
Agent-coordinator
(semantic match engine)
Semantic
annotation
Profile
Shared
ontology
9
Learning resource
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Global Understanding eNvironment (GUN)
Resource
Agent
GUN
Metadata
Shared ontology
10
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,
self-maintained behavior of all kinds of resources that can be adapted to the Web.
http://www.cs.jyu.fi/ai/OntoGroup/
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Agent’s Proactive Behavior in GUN (1)
Able to make diagnostics of
the learner and as result to
know recent profile of the
learner (learner’s state and
condition);
GUN
11
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 .
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Agent’s Proactive Behavior in GUN (2)
Able to check access rights to
appropriate information;
GUN
12
Behaves to maximize the
benefit for the commercial use
of information from the
resource;
Able to navigate external
reader within the resource.
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
From Web-Based Learning …
13
WWW
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
… to GUN-Based Learning.
Semantic Web
14
WWW
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Mechanism of personalization
OR
OL
LOM
PAPI
Learner
OL ,OR
OR
OR
LOM
LOM
OR
OL
LOM
OR
PAPI
Learner
15
LOM
Software agent
Learner
Learning Resource
Metadata
OL , OR
Ontology
Agent Communication Language
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
MOSPDL architecture
16
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
MOSPDL algorithm
The MOSPDL algorithm contains the following
stages:



17

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…
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
MOSPDL algorithm



18
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
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
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
19
4-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:

20

the stage of forming of the profiles for
educational materials
the stage of creation of the needed
educational materials set
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
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:

21

the stage of searching for educational
materials
the stage of individual selection of an
educational material
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Distance learning portal
22
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Learning resource
23
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004
Conclusions
24
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.
4-th IEEE International Conference on Advanced Learning Technologies, Joensuu, Finland, August 30 – September 1, 2004