INTEGRATION OF ONTOLOGY IN DISTANCE
LEARNING SYSTEMS
Models, Methods and Applications
Lina Tankeleviciene and Dale Dzemydiene
Institute of Mathematics and Informatics, Akademijos str. 4, LT-08663, Vilnius, Lithuania
Keywords: Domain ontology, e-Learning, Learning Management System, Learning scenarios.
Abstract: Semantic web technologies, including domain ontologies, can enhance possibilities and functionality of
traditional Web systems. The problem is that these technologies are not fully adopted yet to bring benefits to
final users. We analyse the oppurtunities to integrate domain ontologies into typical learning management
systems in order to gain some automation or support from the system in frequent and time consuming jobs,
which are performed by students and academic staff in ourdays systems. The main aim of our research is to
propose a methodology for the development of the distance learning course domain ontology and its
integration into the distance learning systems. In this paper, we present our research directions and proposed
solutions. Furthermore, we pay here more attention to learning scenarios, which we design considering the
proposed methodology and the particular learning manamement system – MOODLE – in mind.
1 INTRODUCTION
Semantic web technologies, including domain
ontologies, can enhance possibilities and
functionality of traditional Web. For example, Davis
(2007) characterises the business value of semantic
technologies in five critical areas:
 Development – automation in different
development steps;
 Infrastructure – enablement and orchestration
of core resources;
 Information – semantic interoperability of
information and applications in real context;
 Knowledge – knowledge work automation and
supporting knowledge workers;
 Behaviour – systems knows what they are
doing.
Obviously, the technology itself provides only
with increased possibilities. Therefore, methods,
frameworks and tools are necessary for realising
practically all mentioned aims. The problem is that
there are no enough results in using these
technologies, which brings benefits to final users.
Semantic web technologies are usefull in any
knowledge intensive area. We restrict ourselves with
e-learning domain, where Semantic web
technologies – ontologies, agents, web services – are
also employed and intensive researches are carried
in this field (Alsultanny, 2006; Stojanovic et al.,
2001).
The topics of ontology engineering are
comprehensively described in (Devedzic, 2002;
Corcho et al., 2007), including methods and
methodologies for the development of ontologies,
ontology development process and lifecycle,
ontology tools and languages.
Several authors analyse and develop domain
ontologies for specific topics in e-Learning
(Sosnovsky & Gavrilova, 2006; Angelova et al.,
2004). Other papers deal with domain frameworks
or recommendations for ontology development
(Gavrilova et al., 2005; Boyce & Pahl, 2007). After
the analysis of scientific literature, we separate two
types of approaches to the use of ontology in e-
Learning system:
1) Ontology is used at development time.
Automation and reuse are in the main focus (Ateveh
& Lockemann, 2006; Karampiperis & Sampson,
2005; Valkeapää et al., 2007). Here as development
we consider the development of particular study
courses or their elements. Also it would be very
advantageous to actually use ontologies while
designing and developing e-Learning systems, and
later seeking for their interoperability. Such
development process is called an ontology-driven
development of information system, as in (Guarino,
355
Tankeleviciene L. and Dzemydiene D. (2009).
INTEGRATION OF ONTOLOGY IN DISTANCE LEARNING SYSTEMS - Models, Methods and Applications.
In Proceedings of the First International Conference on Computer Supported Education, pages 355-360
DOI: 10.5220/0002163403550360
Copyright
c
 SciTePress

1998). But we have not found any comprehensive
research results in this field.
2) Ontology is used at run time. Users’ support is
in the main focus and ontology is understood mainly
as shared knowledge source and the mean for
achieving personalisation (see, for example,
Angelova et al., 2004).
In this paper, we present our research directions
and proposed solutions for the development of the
distance learning course domain ontology and
scenarios for ontology application in the distance
learning system.
2 RESEARCH PROBLEM
We have practical experience in designing and
delivering distance study courses (DSC) for more
than 6 years. Also one of our functions is to support
academic staff in these processes. So the problems,
which are formulated here, arise not only from
scientific analysis but also from our social
experience. Here we clearly state and analyse some
problems, which we are seeking at least partially to
solve. The purposes, which we are striving to
achieve, are:
 Increasing effectiveness of workload. Not only
the efficiency of the system, but also the
efficiency of activities of its user, both lecturer
and student is actually important. Students
need different kinds of support: technical,
administrative, subject oriented, motivational.
In order to achieve good learning results, a
significant amount of workload time of
instructors is necessary. The ontology-based
description of required knowledge is a
prerequisite while seeking to shift a part of the
student-instructor collaboration processes into
the student-study material level. Therefore,
some functions can be detached from an
instructor and attached to a computer system.
It is important to pick for transfer time-
intensive, frequently repeated, maybe not
complicated functions. In this way, efficiency
will be increased on the organisational level,
too. There is a misleading opinion that self-
supporting studies usually happen effectively.
For this type of studies people with very
strong motivation are necessary. Despite of
very large amount of information, it is
difficult to find, what is useful. Therefore
ontologies and software components, for
example agents, allow us to present study
material in a convenient way for a learner,
avoiding information overload, adapting study
material to the learning style of learner,
readiness of a learner. In practice, learning
scenarios can be generated and thus
personalised learning implemented. This fact
resounds modern ideas of individualised
learning.
 Increasing of satisfaction. Achievements of
students depend on their satisfaction during
gaining learning experience, too. Students
meet modern web technologies in daily life;
therefore, they also expect them in delivering
modern curriculum.
 Adaptivity in dynamic context. Adaptive
systems are concerned as systems, which offer
dynamically built and automatically
performed personalisation. Learning materials
perform changes in time. New learning objects
come; some resources replace the other, some
supplement. The cost of preparing DSC is
conditional big in relation with DSC delivery.
Therefore, we need DSC, which can be reused
in different context.
Relevant problems are analysed in (Dignum &
Dignum, 2003) and the objectives of knowledge
management technologies, considering ontology
modelling is one of them, are stated as follows:
 Assist people to generate and apply “just in
time” and “just enough” knowledge, prevent
information overload and stimulate sharing of
relevant knowledge in a dynamic,
collaborative environment.
 Preserve individual autonomy and contribute to
the creation of an atmosphere of trust between
participants.
Summarising, we can state that the same
problems exist in different subject domains, not only
in e-Learning, where users’ work intensively with
big amount of information. Therefore, supposed
business value of spreading semantic web
technologies, including ontologies, concerns better
support for user in information-intensive
environments, such as e-Learning systems.
Our selected problem domain is e-Learning,
which, strictly speaking, covers participants and
their performed activities. Our solution domain
concerns ontology modelling/engineering and
application. The goal, expressed abstractly, is to
achieve better performance. Therefore, we begin our
analysis from two directions:
1) Analysis of the problems in modern e-
Learning environments, concerning
data/information/knowledge resources used and
processes, in which knowledge management appear.
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The purpose of this part is extraction of possibilities
for qualitative or quantitative change.
2) Analysis of technologies, tools, and
methodologies, concerning ontology
modelling/engineering; design of framework for
change; design of scenarios for innovative use in e-
Learning setting.
Our research object is integration of domain
ontology into the e-Learning system. The objectives
of our research are the following:
 to analyse and compare known approaches to
ontology modelling/engineering related to the
development and improvement of virtual
learning environments (VLE);
 to analyse the ontology quality criteria and the
ontology evaluation methods;
 to propose a methodology for the development
of the distance learning course domain
ontology and its integration into the distance
learning systems;
 to propose an ontology quality evaluation
model for the distance learning domain;
 to develop an experimental domain ontology
for the distance learning course using the
proposed methodology;
 to integrate the domain ontology into the
existing VLE;
 to propose learning scenarios based on the
developed ontology;
 to evaluate the quality of the developed domain
ontology and its use.
Research hypothesis: we expect that extending
LMS with domain ontology will increase tool
functionality, provide more capabilities for effective
learning and self-learning, and allow for rapid
prototyping of DSC.
3 PROPOSED SOLUTIONS AND
RESEARCH DIRECTIONS
3.1 What Have Been Done
3.1.1 Multi-layered Architecture of
e-Learning System Proposed
The multi-layered architecture of the distance
learning system, introduced by us in (Dzemydiene et
al., 2006), integrates components from the common
LMS and extends it with intelligent components by
the means of two architectural layers: 1) Intelligent
layer – intelligent decision support components,
which must act as a mediator between the core LMS
elements and different types of user interfaces; 2)
Deeper knowledge layer – domain, users’, learning
designs’ ontologies, which can act as foundation for
adaptive educational sequencing. Further, we restrict
ourselves with domain ontology as a tool for reuse
on the subject knowledge and a mean for automating
some tasks. And differently from solutions, where
semantically enriched systems are developed from
scratch, as, for example, in our experiment
(Tankeleviciene & Sakalauskas, 2008), we tend to
use a great functionality of modern LMS.
3.1.2 Development of Domain Ontology
Analysed
Development of domain ontology was analysed in
(Dzemydiene & Tankeleviciene, 2008a). As
ontology is “a conceptual specification that
describes knowledge about a domain in a manner
that is independent of epistemic states and state of
affairs“ (Guizzardi, 2007), it can be treated as a
universal model of domain. Therefore, in the context
of e-Learning, we can distinguish: 1) Domain level,
which concerns the domain knowledge. 2) Course
level, which concerns the practical implementation
of e-Learning. The course consists of a set of
learning resources, including both teaching/learning
materials and activities. 3) Technological level,
which deals with learning objects (LO) and
information objects. Such framework allows us also
to distinguish between domain engineering and
course engineering. Therefore reuse can be
employed on a higher level.
In our approach we argue for:
1) Manual ontology development. Despite of the
fact, that there are still much heuristics in the
development of domain ontology manually, it
remains still the best approach to the development of
ontology of high quality. In the near future we have
plans to experiment with semi-automatic methods
for ontology (or its base - taxonomy) development,
because this task is very time consuming.
2) Real schema-based ontology. The differences
between schema-based ontology and topic-based
ontology are explained in (Kiryakov, 2006). The
author accentuates the possibility to formalise the
domain while using a set-theoretical model and set
theoretical operations. We choose schema-ontology
for capturing subject domain knowledge, because: a)
It better corresponds with our understanding of the
concept of ontology; b) It deals with formal or
semiformal representation, and it represents a top-
down systematic approach; c) It better fits in our
instructor-led e-Learning context.
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3.1.3 Reasoning Over Ontology Elements
Analysed
As ontologies are static knowledge resources, we
need active components for performing the tasks.
The concept, possibilities, types and
implementations of reasoning over ontology were
analysed in (Dzemydiene & Tankeleviciene, 2008b).
Also in this paper the framework for conceptual
linking of educational resources, based on reasoning
over domain ontology elements, was proposed.
In our approach we argue for:
1) Query-based reasoning, because it is simpler
and more efficient than logic-based reasoning. We
choose hybrid information systems, where current
web technologies and ontology engineering are
combined.
2) The necessity to implement simpler reasoning
mechanisms over domain ontology in order to
support learner in simple tasks. We strive to achieve
better trade-off between control and self-
responsibility; therefore, conceptual linking of
educational resources and displaying different ways
of reaching the learning goal correspond to our
pedagogical viewpoint.
3.2 Ways for Extending MOODLE
with New Functionality
For our practical experiments MOODLE
(http://www.moodle.org/) was chosen. MOODLE is
a free Learning Management System (LMS) for
offering DSC. The main part of DSC is usually
composed from topics (see Fig. 1 in the middle). For
each topic instructor can define resources and
activities. In our MOODLE version (1.9.2+, last
build 2008.09.17) there are the following types of
resources: a) Label; b) Text page; c) Web page; d)
Link to a file or a web site; e) Directory display; f)
IMS content package.
Figure 1: A part of MOODLE environment in the editing
mode.
The same version by default offers the following
activity types: a) Assignments; b) Chat; c) Choice;
d) Database; e) Forum; f) Glossary; g) Lesson; h)
Quiz; i) SCORM/AICC; j) Survey; k) Wiki.
The right and left hand sides of the DSC page
(see Fig. 1) include blocks that display various
information. Instructor can choose what blocks to
display; some of the most popular blocks are: Latest
News, Upcoming Events, Calendar, Participants.
All these elements – resources, activities and
blocks – provide students with 1) transferring of
knowledge in different forms and in different ways;
2) active participation in learning activities. The
constructivist viewpoint towards learning especially
emphasizes the importance of active participation in
ones own development. For this purpose a great
level of interactivity must be implemented. Here
interaction means primarily the communication
between the user and the system. Interactivity is one
of the criteria or indicators showing quality of
distance studies (Karoulis & Pombortsis, 2003).
These authors emphasize interactivity with the
instructional material, which is described as
navigational fidelity, multimedia components,
multiple kinds of exercises, facilitation of the active
interaction, support for collaborative work and group
dynamics. Students’ support is formulated as other
indicator and it concerns guidance and
encouragement of the student both from the
instructional material and from the communication
channels, accessibility to the tutor, instructional
organisation.
Since MOODLE is an open sourced system,
there are quite many possibilities to extend its
functionality. We can use third party plug-ins or
develop them ourselves; also we can modify existing
elements of a system. Basically modifiable elements
are:
 Modules. They define new learning activities.
 Blocks. Usually they provide extra information
or support for students.
 Inner elements of some module. For example,
question types in Quiz module.
We have found very few efforts to integrate
MOODLE and Semantic web technologies
(Lukichev et al., 2007; Diaconescu et al., 2008).
3.3 Outline of Experiments
After analysis of semantic web technologies and
particular LMS architecture, we have decided to
proceed with experiments in the following areas:
1) Visualisation of domain structure. A tag
cloud as user interface design pattern is planned to
be used. Protégé has plug-in Cloud Views
(http://protegewiki.stanford.edu/index.php/Cloud_Vi
ews), which allows to visualise ontology as a tag
cloud, but this plug-in don’t support yet the export
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of prepared tag cloud. Also we could use some tag
clouds generator, but in this way visualisation of the
domain will be pre-delivered as the DSC starts. Our
idea is to support dynamical linking between domain
ontology and its visual representation. We think
Classes by usage is the best way to accentuate more
important concepts (also we can calculate siblings,
descendants, etc.).
2) Navigational support. The scenario for
providing navigational support is provided by us in
(Dzemydiene & Tankeleviciene, 2008b). Briefly it
consist from several steps: 1) Requested resource is
displayed; 2) The position of the requested resource
in the predefined by lecturer course structure is
displayed; 3) Reasoning over domain ontology and
mappings between ontology concepts and resources
is conducted; 4) Links to resources of possible
interest are generated and displayed. The problem in
implementing this algorithm is related with the
definition of current state, i.e., the position of the
particular resource in overall course structure. The
other problem concerns universality of semantical
linking. We can’t know in advance the possible
intensions of such linking. Therefore, we restrict
ourselves with main linking patterns: 1) Finding all
child concepts; 2) Finding all siblings; 3) Finding all
instances; etc.
For these first two scenarios, the standard
MOODLE HTML block is planned to override.
HTML block provides an HTML editor for
formatting text. Also we can integrate images and
other elements, for example, Flash moves. The
differences in functionality is that the content of
visualisation block is generated once, when student
logs into DSC, and the content of navigational
support block must be generated on every change of
resource (resource view record in log file).
3) Semi-automatic generation of test questions
and their answers. The possibility to append new
question type in Quiz module is foreseen. This
module provides templates for editing questions and
their answers. We are seeking to implement the
support in development of questions by the means of
suggesting ontology components, for example,
labels of classes, and names of instances or relations.
We plan to use the following question types: 1)
Multiple choice – where one or more true answers
are must be chosen; 2) Short answer – where people
must type a word; 3) Matching – where relations
between elements in two column must be found.
4 CONCLUSIONS AND FUTURE
WORK
Semantic web technologies are rapidly evolutioning,
therefore, it is a great demand for analysis of the
latest achievements and striving to use them in
existing solutions – information systems from
different fields.
The approach to using domain ontology in the
development and delivery of educational resources
enables automating these processes, increasing
adaptivity and personalisation. Scenarios for those
tasks are foreseen. The first two scenarios for
ontology usage are more oriented towards learner
satisfaction and higher learning effectiveness,
because they introduce learners to domain space and
provide more possibilities for personal navigation.
The third scenario is oriented towards automation of
workload of academic staff. The main shortcoming
of this approach is that it requires large efforts of
humans at initial stage, and we will benefit from this
approach only after its repeatable reuse, but, on the
other hand, this problem concerns distance studies
and e-Learning in general, too.
In order to evaluate the proposed methodology
and designed learning scenarios, we shall implement
and test these scenarios in a particular LMS.
Our future research work also will focus on the
analysis and formulation of the detailed list of the
semantical linking patterns, derivation of possible
patterns of test questions from semantical linking
patterns, and implementation of experimental tools
for realisation of these patterns.
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