1. 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, ISTAS IEEE International Symposium on
Publication Date: June 2008

2. Outline
Introduction
Defining Learning Objects in a Cooperative Knowledge Space
Semantic Network of Learning Objects
Modularization and Re-Combination of Learning Objects

3. 1. Introduction
most of these systems focus on providing content and do not support cooperative learning and work scenarios
there are platforms that mainly concentrate on the user-centered and communicative aspects of learning for the most part disregarding standards-compliant 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.

4. 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

5. 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

6. 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.

7. 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

8. We provide the following association types:
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.

9. 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 re-
combined in a new context as a SCORM
course.