Integrating Ontologies into the Collaborative Authoring of Learning
Juan Manuel Dodero
(Universidad Carlos III de Madrid, Leganes, Spain
(Universidad Carlos III de Madrid, Leganes, Spain
(Universidad Carlos III de Madrid, Leganes, Spain
(Universidad Complutense de Madrid, Madrid, Spain
Abstract: Authoring learning material is a multi-disciplinary
undertaking where different people can play their role. Any support that
can be provided for the collaboration of instructional designers, pedagogues,
media designers, and students, among others, is welcome. In particular,
metadata annotation of learning objects is an important task within the
whole authoring process. This work presents the first resulting products
and approaches from the MD2 project, consisting of a service-oriented framework
and a tool to support the integrated, ontology-based collaborative annotation
of learning objects.
Keywords: Learning objects, metadata, ontologies, collaborative
Our current Society is constantly involved in a permanent evolution
of knowledge and new educational models aim to provide solutions to its
challenges. The constructivist visions of education [Jonassen,
1999] claim a participative role for the learner. Learning Management
Systems (LMS) are key tools to support new educational models. But traditional
LMS lack the required flexibility and adaptability to implement constructivist
educational models. Any constructivist approach implies involving learners
further in the instructional process. In particular, many different roles
can participate in the creation of learning material [Polsani,
2003], including instructional designers, pedagogues, instructors,
media designers and students. For this reason and due to its multidisciplinary
nature, any support for collaborative participation is welcome.
The authoring of learning objects [Milligan, 2003]
and annotation tools [Magee et al., 2002] provide
scant support for collaborative authoring, annotation, or edition. They
bind users to editing content isolated from the rest of the team and, in
the best cases, provide a basic version control mechanism. However, versioning
usually works at the package or file level, but not at the content level.
Collaborative authoring at the content level can be supported by complementary
discussion forums, but the results of discussions may not necessarily be
easily committed as changes into the final contents.
The goal of this work is to provide an integrated solution to collaborative
authoring for the creation of learning material. In this way, the MD2 project
aims to provide a framework, method, and a set of tools that can help authors
carry out several collaborative tasks involved in the creation of reusable
learning material. Among others these tasks are: editing changes, proposing
annotations, sending and discussing proposals, providing assessments, conciliating
different proposals and carrying the final decision to the learning object.
Of course, the development of a general-purpose collaborative authoring
facility for learning objects is a fairly ambitious goal. Therefore, firstly
we have limited the approach to providing support for metadata annotations.
Nevertheless, the overall architecture and software tools have been designed
to provide a general-purpose collaborative authoring system.
In order to improve reusability of learning objects, metadata annotation
is an essential task. If we consider automatic and dynamic composition
of learning objects with a pedagogical purpose, it becomes clear that the
computer should have access to information regarding the design of instructional
material [Wiley, 2002]. Metadata annotations are the
vehicle that transports this kind of information, and they should be considered
as an essential authoring task. In a broad sense, annotation is considered
as the act of adding extra information associated with a particular point
in a document. Nevertheless, we restrict annotations to a non-linguistic
form, and they must be made to a learning object (e.g. learning object
metadata [Thropp and McKell, 2001], or other structured
descriptive information models, like IMS Learning Design [Koper
et al., 2003]).
However, metadata annotation is usually an arduous and not often a successfully
completed task, despite the fact that metadata specifications are mostly
mature and a number of tools are readily available. To alleviate this,
a collaborative annotation approach is taken to share metadata annotation
tasks among a group of asynchronous and distributed authors. On the other
hand, current specifications for learning object metadata (LOM) are not
fully prepared to semantically represent rich information about the design
process of instructional material. LOM and related specifications cannot
be readily used to annotate a learning resource to express a design restriction,
or a rationale occurring during its design. The common approach here is
to extend metadata with the richer semantic support provided by ontologies
The rest of the paper is structured as follows: Section
2 describes the main goals of the work and the overall architecture
of our solution; Section 3 focuses on the collaborative
annotation module and its role within the software platform; Section
4 describes the issues that arise when external ontologies are integrated
to augment the base of annotations; and finally Section
5 states some conclusions and future lines of work.
2 MD2: An Integrated Approach to the Collaborative
Authoring of Learning Objects
The purpose of the MD2 project is to provide solutions to major issues
that arise during the creation of learning material. The main objectives
of the project are the following:
- The development of a method and a set of tools for the collaborative
authoring of learning contents that can offer a framework for constructive
learning and creation of knowledge with a view to improve efficiency and
reduce the efforts of coordination.
- The extension of current learning objects specifications to improve
reusability through metadata cohesion by means of shared and agreed on
The sought-after authoring facilities of MD2 are provided by CARLOS1,
a collaborative and integrated development environment (IDE) used to author
reusable learning objects. Figure 1 depicts the context
of CARLOS authoring tools. Such IDE can be integrated with any IMS-compliant
LMS that provides required external services, such as a learning object
run-time engine, an index and search service, or any user modelling and
Figure 1: Context of the collaborative authoring environment
The overall architecture of the CARLOS software platform is portrayed
in Fig. 2. Next, a brief description of its modules
and functionalities are presented.
- Edition + Annotation: these modules provide the basic functionalities
for editing and annotating learning objects. Both are integrated into a
unique tool, but enhanced with capability extensions to consider transversal
aspects served by other modules, like ontology import and collaboration
- D-Ontology Import: this module allows for the extension of the
annotation vocabulary by using RDF(S) domain ontologies.
CARLOS stands for Collaborative
Authoring of Reusable Learning Objects System
- Collaboration: this module supports the collaboration protocols
and mechanisms during the development of the learning object [Dodero
et al, 2002].
- Assessment: this module provides the means to perform quality
tests for the learning object in-development [Sarasa and
Dodero, 2004]. It is tightly integrated with the collaboration module.
- Performance Analysis: this process carries out an analysis of
the behavior of learning objects users during the didactic process in order
to evaluate their performance in a given learning context. It takes into
account the user model and the LMS run-time engine. The results of user
performance is reverted into proposals for further refinement to the learning
- Refactoring Observer: this asynchronous system takes the values
and annotations generated by the performance analyzer as input, and generates
proposals to re-design the learning object (i.e., further annotations,
refinement of objectives and/or requisites, recommendations for new examples,
splitting or merging contents, etc.).
Figure 2: General architecture of the CARLOS platform
The remaining architectural components of Fig. 2
(i.e. LMS, learning object repository and shared ontology server) are external
subsystems that must be adapted to the development platform in order to
take advantage of their services. Web services are used to accomplish the
integration of such components and systems in an integrated architecture
[Padrón et al., 2004].
We have first developed a collaborative annotation tool to take advantage
of the main modules, i.e. annotation, ontology import, collaboration, and
assessment. The annotation tool operates over the available web service
infrastructure. The tool was conceived as a general-purpose collaborative
authoring system to develop XML documents, according to any predefined
schema. Its pattern-based design [Gamma et al., 1995]
reduces the possible dependencies that learning objects specification schemata
might impose, and also allows collaboration protocols and evaluation strategies
to be dynamically plugged-in and out.
Figure 3: Role of the collaborative annotation server in
3 A Service-Oriented Architecture for Collaborative
The collaborative annotation facility relies on a web service collaboration
gateway and a collaboration service provider, which are depicted in Fig.
3. The front-end translates proposals and notifications of change to
the adequate web service primitives by using WSDL descriptions. On the
other hand, the back-end server works as the collaboration provider. Although
it has been implemented as a centralized server, it is also feasible to
integrate its services into the front-end part. This way, a peer-to-peer
collaboration infrastructure can be built without any loss of functionality.
3.1 Annotation tool
The front-end annotation tool provides the interface to annotate any
learning object selected from the repository. The front-end is developed
and deployed independently from the back-end collaboration server. It must
only consider the WSDL published interface and interact with the appropriate
web services. In this way, different front-end tools can be developed to
profit from the collaboration server.
Figure 4: Operation of Vizzini thin-client on a learning
Currently, a thin-client application nicknamed Vizzini is available
for the front-end, giving access to all the functions of the collaboration
services. Every annotation is carried through the collaboration server
before being applied to the learning object manifest that is being edited
(see Fig. 4). The upper right panel shows the current
state of the manifest file with the selected annotation, the upper left
panel shows a tree-structured collection of annotations, the lower left
panel depicts the collaborative activity log, and the lower right panel
contains the pending and fulfilled tasks and assessments for the selected
annotation. However, our thin client does not provide a complete authoring
environment for learning objects. For that aim, a plugged-in extension
to Reload editor [Milligan, 2003] is being
3.2 Collaboration services
The collaboration back-end allows users to annotate a learning object
after negotiating and evaluating annotation proposals. Two web services
have been provided for this:
- The main collaboration web service accepts the collaboration protocol
messages and also deals with issues that are related to the management
of users, projects, and negotiations.
- The second web service monitors the pending tasks, which are mainly
assessments for negotiations in which users take part.
Every interaction is automatically negotiated and the result is
included in the appropriate section of the manifest file. As a side
achievement, the whole collaboration process is logged and registered
in a version control system, making it possible to trace the
annotations that have been carried out.
4 Integration of Ontologies in Annotation
The second goal of MD2 is to extend current learning object
metadata potential to improve reusability by means of shared and
agreed ontologies. In this sense, deriving meaning from contemporary
web and learning resources is nearly impossible without a common
metadata framework for describing such resources ¾that is the
rationale behind the semantic web [Berners-Lee et
al., 2001]¾. Metadata are used to describe, certify,
annotate, extend or keep an updated history of a given learning
object, and represent an interpretation of resources for a
machine-understandable layer (e.g. software agents, sophisticated
search engines, or web services) that can facilitate their automated
processing. Ontologies aim at capturing and providing a commonly
agreed understanding of a given domain and play an important role as a
shared source of formally defined concepts for communication. Thus,
ontology annotations are commonly used to access learning objects and
services from distributed repositories and present them to the users
according to the learning context.
Metadata annotations are usually made according to LOM, which
distinguishes different categories (i.e. general, technical,
educational, classification, etc.) to describe a learning object. The
classification category is used particularly to accommodate
annotations related to a given classification scheme (e.g. the Dewey
decimal classification system [Dewey, 1983] or
any other taxonomy). In our work, the elements taxon and
taxonpath from the classification category are chosen for
cataloguing resources with domain-specific information. It must be
noted that this is a limited solution, since current LOM
specifications are not prepared for full-fledged ontologies that can
be represented by description logics [McGuiness and
Van Harmelen, 2004].
Considering that the purpose of shared ontologies is the development
of conventions to support the sharing and reuse of knowledge among systems
[Patil et al., 1992], it seems reasonable to think
of them as an appropriate basis for performing the annotation of learning
objects. In order not to constrain the future evolution of ontology annotations,
we have used RDF(S) as the annotation language and wrapped RDF instances
as taxon elements. This has been done according to the LOM to RDF binding
[Nilsson et al., 2003].
The annotation tool must comprehend external vocabularies to be able
to submit annotations that are specific to a given domain. This task is
carried out by the D-Ontology Import module. Imported ontologies are classified
into several shared namespaces.
Domain specific ontologies are easily imported
as RDF(S) descriptions and wrapped into the manifest file. In case the
referred ontologies are not available online, their RDF schemata are packaged
as resource files along with the learning object. External ontologies used
to support the instructional design and authoring process are managed through
external components and interfaces that provide navigation and edition
capabilities through the domain ontology concepts [Broekstra
et al., 2002].
5 Conclusions and future work
In this paper we have presented an integrated framework for collaborative
authoring and annotation of learning objects, which is being developed
within the MD2 project. For this goal, a collaborative IDE of learning
objects has been developed. We have also discussed some issues related
to the integration of ontologies in learning object annotation.
The hypotheses of the MD2 project are three-fold. On the one hand, we
think that collaboration can help to reduce the effort for the development
team, since annotation tasks can be more easily distributed among the development
team. On the other hand, the collaborative annotation process should help
to improve the quality and reusability of learning objects. Finally, we
think that collaborative annotations can facilitate the constructivist
approaches of learning, as long as learners and instructional designers
can be jointly involved in the development of learning material. Future
work is aimed towards corroborating these hypotheses through field and
This work is part of the MD2 project (TIC2003-03654), funded by the
Spanish Ministry of Science and Technology. We also wish to thank the rest
of MD2 team, namely Telmo Zarraonandía, Carmen Padrón, Jorge
Torres, and Jesús Lanchas for their valuable contribution to this
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