Int J Digit Libr (2008) 9:41–63
DOI 10.1007/s00799-008-0039-8
REGULAR PAPER
ALOCOM: a generic content model for learning objects
Katrien Verbert · Erik Duval
Published online: 11 June 2008
© Springer-Verlag 2008
Abstract e-Learning organizations are focusing heavily on
learning content reusability. The ultimate objective is a learn-
ing object economy characterized by searchable digital
libraries of reusable learning objects that can be exchanged
and reused across various learning systems. To enable such
approach, basic questions of learning content interopera-
bility need to be addressed. This paper investigates the inter-
operation of learning content defined according to different
specifications. A number of content models are reviewed that
define learning objects and their components. On the basis of
a comparative analysis, the content models are mapped to a
generic model for learning objects to address interoperability
questions and to enable share and reuse on a global scale.
Keywords Content models · Ontologies · Reusability ·
Interoperability
1 Introduction
Barriers and enablers for the reusability of learning objects
are important research topics in the learning technology com-
munity. In various publications, it is argued that reuse not
only saves time and money [11,34], but also enhances the
quality of digital learning experiences, resulting in efficient,
economic and effective learning [13].
There is an inverse relationship between the size of a
learning object and its reusability [45]. As the size of the
K. Verbert (
B
) · E. Duval
Department of Computer Science, K.U. Leuven,
Celestijnenlaan 200A, 3001 Leuven, Belgium
e-mail: katrien.verbert@cs.kuleuven.be
E. Duval
e-mail: erik.duval@cs.kuleuven.be
learning object decreases (lower granularity), its potential for
reuse increases. Size is thus an important factor for enabling
successful learning object reuse. However, this size is only
vaguely defined by learning object definitions [35].
According to the learning object metadata (LOM) stan-
dard, a learning object is “any entity, digital or non-digital
that may be used for learning, education or training” [12].
This definition allows for an extremelywide variety of granu-
larities [35]. In one sense, this is appropriate, as there are
a number of common themes to content components of all
sizes. In another sense although this vagueness is problem-
atic, as it is clear that authoring, deployment and repurposing
are affected by the granularity of the learning object [13].
Learning object content models address this problem. The
models define different kinds of learning objects at differ-
ent levels of granularity and are based on the belief that we
can create independent and self-contained learning content,
which may be used alone or dynamically assembled, to pro-
vide “just enough” or “just-in-time” learning. On top of that,
these learning components can be combined to form longer
educational interactions or reused in different learning con-
texts [40].
However, there are many different content models and
learning object definitions across these models vary consi-
derably. Some models define learning objects as lessons,
while others relate learning objects to concepts, principles,
facts, procedures or processes. The heterogeneity of defini-
tions is a barrier for learning content reuse on a global scale,
as it is unclear whether content can be reused or repurposed
in a different context.
In an earlier work, we developed an abstract learning
object content model (ALOCOM) for content model inter-
operability [42]. On the basis of the definition of the NETg
[22], SCORM [37], Cisco [3] and Learnativity [44] con-
tent models, we specified a model that defines three general
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42 K. Verbert, E. Duval
aggregation levels. The models were mapped to our model
to address interoperability questions.
In this paper, we present an extended version of this work.
Nine content models have been analyzed in detail. To enable
their interoperability, an ontology has been developed that
builds upon our earlier work. The abstract learning object
content model has been detailed, specifying content classi-
fications and relationships between content components and
mappings have been implemented between content models
according to the method introduced in [6]. The method has
three main stages:
− building a global ontology that covers existing content
models
− building local ontologies for each content model, and
− defining mappings between the ontologies.
Mappings can enable share and reuse of learning objects
across digital libraries. Learning object components stored
in an SCORM digital library can, for instance, be identified
and potentially repurposed in the context of a Cisco or NETg
learning system.
To facilitate the description and comparison of learning
object content models, we first briefly introduce content clas-
sification schemes that are used by the investigated content
models for defining granularity levels. In Sect. 3, the content
models that were included in the investigation are presented
and Sect. 4 presents a comparative analysis. The method
used for implementing mappings is described in Sect. 5. The
global ALOCOM content model is presented in Sect. 6, local
content model ontologies in Sect. 7 and mappings in Sect. 8.
How this work connects to RAMLET [31], an IEEE stan-
dard under development, is discussed in Sect. 9. Use cases
are described in Sect. 10 and related work is discussed in
Sect. 11. Finally, conclusions and remarks on future work
conclude this paper.
2 Background
Learning object contentmodels define different levels of con-
tent components, the properties of these components, such
as granularity, and how these components can be aggre-
gated [35]. To define granularity levels, different classifica-
tion schemes are used by current content models, such as the
structured writing methodology developed by Horn [18] or
the classification of Ballstaedt [2]. To facilitate the descrip-
tion and comparison of content models, we briefly introduce
the classifications in this section.
2.1 Structured writing
The structured writing method of Horn [18] was developed
for instructional developers and business writers to prepare
clear and concise training manuals, proposals, reports and
memos. The methodology should enable managers, sales
people, office personnel, and technicians to learn new prod-
ucts, services, and operating procedures rapidly and pre-
cisely.
In the methodology, a paragraph is replaced by an infor-
mation block, a chunkof information that is organized around
a single subject, containing one clear purpose. Horn defined
200 types of information blocks, including analogy, block
diagram, checklist, classification list, classification table,
classification tree, comment, cycle chart, decision table, def-
inition, notation, objectives, outlines, parts-function table,
parts table, prerequisites to course, procedure table, purpose,
rule, synonym, and theorem.
In addition, a set of content analysis categories and ques-
tion types were defined based on seven information types
[17]:
1. Concept: A “concept” describes an abstract or generic
idea generalized from particular instances. A concept is
used for teaching a group of objects, symbols, ideas, or
events which are designated by a single word or term,
share a common feature and vary on irrelevant features
[3].
2. Fact: A “fact” provides information based on real occur-
rences; it describes an event or something that holdswith-
out being a general rule [41].
3. Classification:A “classification” is a sorting of items into
categories. A typical example is “overview of technolo-
gies within medical imaging” [8].
4. Structure: A “structure” is a physical object or some-
thing that can be divided into parts and has boundaries.
A typical example is “the anatomy of the human brain”
[8].
5. Principle: A “principle” is a basic generalization that is
accepted as true and that can be used as a basis for rea-
soning or conduct [41].
6. Procedure: A “procedure” consists of a specified
sequence of steps or formal instructions to achieve an
end. Typical examples are “Euclid’s algorithm” or
“instructions to operate a machine” [3,41].
7. Process: A “process” describes a sequence of events. A
process provides information on a flow of events that
describes how something works and can involve several
actors. Typical examples are “the process of digestion”,
and “how a computer system responds to commands”
[3,41].
Guidelines were developed that identify which key infor-
mation blocks are necessary to fully understand a topic. The
underlying research focused on a deep understanding of the
basic units of a subject matter and provides an easy to under-
stand taxonomy. Developed in 1967, structured writing can
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