Ontology-based Learning Content Repurposing
Katrien Verbert
Dept. Computerwetenschappen,
Katholieke Universiteit Leuven
Celestijnenlaan 200A,
B-3001 Leuven, Belgium
Katrien.verbert@cs.kuleuven.ac.be
Dragan Gašević, Jelena Jovanović
FON-School of Business Administration,
University of Belgrade
POB 52, Jove Ilića 154, Belgrade,
Serbia and Montenegro
gasevic@yahoo.com, jeljov@fon.bg.ac.yu
http://goodoldai.org.yu
Erik Duval
Dept. Computerwetenschappen,
Katholieke Universiteit Leuven
Celestijnenlaan 200A,
B-3001 Leuven, Belgium
Erik.duval@cs.kuleuven.ac.be

ABSTRACT
This paper investigates basic research issues that need to be
addressed for developing an architecture that enables repurposing
of learning objects in a flexible way. Currently, there are a
number of Learning Object Content Models (e.g. the SCORM
Content Aggregation Model) that define learning objects and their
components in a more or less precise way. However, these models
do not allow repurposing of fine-grained components (sentences,
images…). We developed an ontology-based solution for content
repurposing. The ontology is a solid basis for an architecture that
will enable on-the-fly access to learning object components and
that will facilitate repurposing these components.
Categories and Subject Descriptors
K.3 [Computing Milieux]: Computers and Education; H.3.m
[Information Storage and Retrieval]: Miscellaneous
General Terms: Learning objects
Keywords: Content models, metadata, ontologies,
repurposing

1. INTRODUCTION
Learning objects (LOs) and their reusability are one of the most
important current research topics in the learning technology
community [1]. Reusability of LOs is conventionally regarded as
the use of entire LOs in different contexts. The Learning Object
Metadata (LOM) standard [2] provides a set of metadata elements
for describing LOs: this facilitates finding relevant LOs.
However, in many cases we need to reuse specific parts of a LO,
rather then the LO as a whole. In such situations, current practice
is to copy & paste in order to reuse specifically those parts of a
document (e.g. a definition, an example or an illustration) that are
relevant. However, this can be rather tedious and time-consuming.
More important, such an approach is non-scalable in terms of
maintenance, since each time you copy a content unit, a new place is
created that needs to be maintained. Our goal is to release authors
from the task of reusing parts of LOs manually, by automating
that process as much as possible. Therefore, we need a LO
content format that includes an explicit definition of the structure
of the LO. We developed an ontology that provides an explicit
definition of the LO content structure, formally specifying both
LO component types and relationships between those
components. Furthermore, we need tools for
extracting/transforming LO content into this ontology content
format (we call those tools disaggregators) as well as tools for
repurposing ontology-aware content in real-world applications.
This approach will enable not only repurposing of complete LOs,
but also the retrieval and repurposing of relevant components.
In the next section, we briefly outline the ALOCoM ontology.
Section 3 illustrates the role of the ontology in the process of
authoring learning materials. Section 4 elaborates on tool support
and conclusions and remarks on future work conclude this paper.
2. THE ALOCOM ONTOLOGY
We developed a generic content model (ALOCoM) that defines
LOs and their components [7]. The model differentiates between
Content Fragments (CF), Content Objects (CO), and Learning Objects
(LO). CFs are content units in their most basic form, like text, audio
and video. Basically, CFs are raw digital resources. They can be
further specialized into discrete (graphic, text, image) and continuous
(audio, video, simulation and animation) elements. COs aggregate
CFs and add navigation. Navigation elements should enable proper
structuring of CFs within a CO. Besides CFs, a CO can include other
COs as well. At the next aggregation level, a LO is defined as a
collection of COs with an associated learning objective.
We defined content types for each of these components. We
introduced CF types such as images, text, audio and video. For
defining CO types, we investigated existing Information
Architectures, like the Information Block Architecture [4] developed
by Dr. Horn and the IBM Darwin Information Typing Architecture
[6]. These architectures define information types (e.g. concept,
principle, task) and their building blocks (e.g. example, definition,
analogy). As a starting point, we defined CO types and their
structure using DITA concepts, since DITA is a recent architecture
with rich documentation and online support [6]. Besides CF and CO
types, the ontology identifies LO types. For now, only a slide
presentation type is defined. Finally, the ontology defines the
relationships between the LO components. Both aggregation and
navigation relations are specified. For more details about the
ontology, we refer to [7].
3. THE ROLE OF THE ONTOLOGY IN
THE AUTHORING PROCESS
A teacher (or another kind of author) uses an authoring tool to
produce learning materials for students. Since our aim is to
facilitate the process of learning content authoring, we provide the
teacher with the technology that enables him/her to reuse existing
components deposed in LORs. In our model, the LOR should
provide access not only to complete LOs, but also to smaller
components, i.e. COs and CFs as they are defined in ALOCoM.
In order to have LORs with ALOCoM ontology-aware content,
we transform LOs into a form compliant with the ALOCoM
ontology. That is why we need tools we call disaggregators. They
take as input LOs in any domain/tool specific format and convert
them into an ALOCoM compliant output format. Having learning

Copyright is held by the author/owner(s).
WWW 2005, May 10-14, 2005, Chiba, Japan.
ACM 1-59593-051-5/05/0005.

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