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Modularization and Semantics of
Learning Objects in
a Cooperative Knowledge Space
Nadine Ludwig
Center for Multimedia in eLearning & eResearch, Berlin Inst. of Technol., Berlin;
This paper appears in: Technology and Society, 2008. ISTAS 2008. IEEE
International Symposium on
Publication Date: 26-28 June 2008
Outline
1. Introduction
2. Defining Learning Objects in a
Cooperative Knowledge Space
3. Semantic Network of Learning Objects
4. Modularization and Re-Combination of
Learning Objects
1. Introduction
1.
most of these systems focus on providing content
and do not support cooperative learning and work
scenarios
2.
there are platforms that mainly concentrate on the
user-centered and communicative aspects of
learning for the most part disregarding standardscompliant content creation and modularization.
This article will describe a potential solution to this
problem by using the advantages of both types of
platforms by merging and complementing their
functions.
2. Defining Learning Objects in a
Cooperative Knowledge Space
The Learning Object Metadata (LOM) developed by the LTSC
enables outsiders to see what the object represents without
having to execute it.
The LOM element set consists of nine metadata categories:
1. General Category
2. Lifecycle Category
3. Meta-Metadata
4. Technical Category
5. Educational Category
6. Rights Category
7. Relation Category
8. Annotation Category
9. Classification Category
For uploading laboratory applets, a special form is
provided where the user can describe which devices
and parameters are included and the applet file (*.jar)
can then be uploaded
3. Semantic Network of Learning Objects
The semantic network of the objects will be provided in two ways:
• By behavior and position within the cooperative knowledge
space
• By contextual coherence
On the one hand the objects will be observed regarding their position
and behavior in the system.
To store this information, the XML topic map standard in its 2nd
version will be used.
The following topic types will be provided in the cooperative
knowledge space:
• Document (externally produced)
• Content page (internally produced)
• Binary object (pictures, videos etc.)
• Laboratory (Java applet)
• Room
• User
To bring these topics into a semantic coherence,
they have to be associated to each other.
We provide the following association types:
• User knows User (are “Buddies”)
• User created/read Document
• User implemented Laboratory
• User owns key for Room
• Document belongs to Laboratory
• Content Page is linked to Content Page
• Content Page is linked to Room
The ontology is implemented using the ontology defining
language OWL and is also stored in the relational database.
4. Modularization and Re-Combination of
Learning Objects
The topic map and the ontology help find
relevant objects or give further information
on a special topic or a lecture. In that way,
learning objects can act like modules of
different learning courses and can be recombined in a new context as a SCORM
course.