Semantic Web Technologies Applied to e-learning Personalization
(Universidad Complutense de Madrid, Madrid, Spain
(CES Felipe II, Aranjuez, Madrid, Spain
(Universidad Complutense de Madrid, Madrid, Spain
Abstract: Despite the increasing importance gained by e-learning
standards in the past few years, and the unquestionable goals reached (mainly
regarding interoperability among e-learning contents) current e-learning
standards are yet not sufficiently aware of the context of the learner.
This means that only a limited support for adaptation regarding individual
characteristics is currently being provided. In this article, we propose
the use of semantic metadata for Learning Object (LO) contextualization
in order to adapt instruction to the learner's cognitive requirements in
three different ways: background knowledge, knowledge objectives and the
most suitable learning style. In our pilot e-learning platform (<e-aula>)
the context for LOs is addressed in two different ways: knowledge domain
and instructional design. We propose the use of ontologies as the knowledge
representation mechanism to allow the delivery of learning material that
is relevant to the current situation of the learner.
Keywords: Hypermedia systems, Web-based services, XML, Semantic
Categories: H.3.1, H.3.2, H.3.3, H.3.7H.5.1
E-learning standards are now starting to have a real impact in the Web
based field of education. Their use is not only widely extended in pilot
and institutionally funded experimental applications, but many commercial
platforms such as WebCT or Blackboard are beginning to be partially compatible
with some of the standardization proposals as well. Nevertheless, most
of the efforts made and the goals achieved deal with content interoperability
(learning objects -LO-, assessment and course packaging). Under current
standards and practices, valuable information needed for adapting learning
to individual characteristics cannot be easily harvested [Rodriguez,
Therefore the question is, how can we make e-learning aware of the individual
characteristics of the learner? How can we adapt a pedagogical e-learning
strategy that best fits an individual in a standard way or, at least, in
a standard compatible way?
This paper proposes an approach to the annotation of LOs, which is based
on open standards such as Learning Object Metadata (LOM [IEEE
LOM, 02]) and semantic web related technologies, such as knowledge
representation by ontologies and Resource Description Framework (RDF [W3C
The long term goal of our <e-aula> project is to dynamically generate
on-line personalized learning through the assembly of atomic learning assets
into coherent learning activities based on the following learner cognitive
particularities: background, objectives and learning style. The idea is
to select and combine LOs at runtime to generate a personalized course.
To make this goal feasible, the first step is to annotate LOs with more
profound information in two different areas:
- Pedagogically relevant information to capture the instructional function
of a LO. This kind of information will enable LOs to have a description
from a teaching and learning perspective, an aspect which is not yet fully
covered by e-learning metadata standards. The educational category
of LOM allows a description of resources from an instructional perspective
with values provided by a list (including Diagram, Figure,
Table, Exercise, Narrative, Text and Exam),
thus it does not take into account any inherent structure that may exist
among the different terms. Even more critical is that types of LOM mix
instructional and technical information, and hence should be separated
- Context domain information. Metadata about the content of a learning
resource would provide for better search results.
In this paper we propose the use of two different ontologies to capture,
on the one hand, the instructional function of a LO and, on the other hand,
the context of the knowledge domain the LO belongs to. This kind of representation
offers the well-known advantages of ontologies: it can provide humans with
a shared vocabulary and can serve computers as the basis for semantic interoperability.
This paper is structured as follows: in section
2 we present the general architecture of how personalization is
addressed in the <e-aula> system. The processes and the system
components involved in the adaptation mechanisms are also
described. In section 3 we describe the adaptation
logics of the system in more detail. In section 4
some related works are described. Finally, conclusions and future work
are presented in section 5.
2 <e-aula> Architecture for Personalization
<e-aula> is a research e-learning platform that aims to assist
final users in the various processes involved in the e-learning experience
(for more details about <e-aula> see [Sierra, 05]
or [Sancho, 04 b]). This platform covers the whole
process from content creation to its final presentation to the learner.
In addition, several tools are being developed to assist the different
steps involved in this global process.
The system's objectives are:
- From the learner's perspective, to dynamically deliver personalized
content to the learner's cognitive characteristics in terms of his/her
knowledge goals, his/her previous knowledge and his/her most suitable learning
- From the content author's perspective, to assist the course creation
process enabling reusability of existing learning materials and making
LO selection easier according to pedagogical criteria.
The <e-aula> architecture addressing personalization features
is based on the following pillars (a similar approach is described in [Schmidt,
- Breaking down courses into modular units (i.e. learning objects).
- Modelling the LO context. The model of the LO context that is addressed
in <e-aula> through the use of two different ontologies, includes:
(a) The pedagogical context, which means the instructional functionality
of the LO from a teaching and learning perspective.
(b) The knowledge domain of the LO. In <e-aula> this domain is initially
restricted to programming languages.
Figure 1: <e-aula> Overview of Personalization Architecture
- Annotating learning objects with pedagogically relevant information,
using e-learning standards when possible (e.g. LOM) and the terms of the
ontologies to make semantic and didactical relationships between learning
objects explicit according to the context domain and the instructional
design of the course. This additional information has to be standard compatible.
- Employing user modelling techniques to obtain different learner models
or profiles according to which personalization might occur.
- Adapting the learning content to fit the user context using learner
model information and the semantically rich metadata associated with learning
A general overview of the system's personalization architecture is shown
in Figure 1.
The system deals with LOs, which in this context are defined as the
minimal units of pedagogically reasonable learning content (like text,
pictures, animations, examples...) and the metadata that describe them.
As a part of the <e-aula> project, a personalized course will be
built for a learner through a set of these LOs. This is carried out in
accordance with the learner's objectives and cognitive peculiarities by
using a metadata driven approach. We are using a restricted set of LOM
categories plus some extensions that are required by our personalization
goals. These extensions are defined by two different ontologies: the pedagogical
ontology and the context of the knowledge domain.
Based on the information provided by the ontologies and the user profile
elaborated by the user modelling process (Figure 2),
a personalized course will be created. The instructional design of the
personalized course is modelled according to IMS Learning Design specification
[LD, 03] and covers the most suitable learning style
according to the Felder-Silvermann classification [Felder,
88] and the learner's knowledge objectives.
The final step for the adaptation process is to equate the learning
content with the learner's prior knowledge. This content adaptation is
made by XSLT transformations of LO content. A further description of all
these processes is given in section 3.
3 The Ontology Driven Personalization Approach in
Personalised learning systems bear the potential to meet the requirements
of the knowledge society for high quality education and training [Brusilovsky,
99], because they are capable of automatically adapting to the changing
attributes of the learning experience. The adaptation logic of a personalised
learning system can be defined in the following terms [Karagiannidis,
- The determinants: the aspects of the learning experience which drive
the adaptation. What is the adaptation based on?
- The constituents: the aspects of the learning experience that are subject
to adaptation. What is being adapted?
- The rules: the logics which define which constituents are selected
for different determinants.
In the following sub sections these three aspects of the <e-aula>
system are addressed.
Figure 2: Adaptation process in <e-aula>
3.1 <e-aula> Adaptation Determinants
In <e-aula> we consider three different aspects of the learner
cognitive particularities for adaptation:
3.1.1 Learner knowledge goals
Our long term goal is to offer the learner a competency-based e-learning
experience according to a life-long learning educational paradigm [Koper,
Depending on the learner's knowledge objectives, very different pedagogical
strategies need to be applied. In <e-aula> we are initially considering
only three different objectives and we are using IMS LD for instructional
modelling (see section 3.3 for further details):
- Quick overview. In case the learner wishes to obtain a quick overview
of a topic or get a global idea.
- Average. All the concepts are exposed together with several examples
and some additional material to give the learner the opportunity of gaining
what we consider is just an intermediate level of knowledge.
- Expert. In case the learner wants to gain a full understanding of the
topic, all the concepts plus all the additional material including examples
and simulations are accessible to the learner.
||Learning Style 1
||Learning Style 2
||Sensing: Concrete and practical, oriented toward
facts and procedures.
||Intuitive: Conceptual and innovative,
oriented toward theories and meanings
||Visual: Prefers visual representations:
pictures, diagrams, flow charts.
||Verbal: Prefers written and spoken explanations.
||Inductive: Prefers representations that
proceed from the specific to the general.
||Deductive: Prefers presentations that
go from the general to the specific.
||Active: Learns by trying things out and
working with others.
||Reflective: Learns by thinking things
through, generally working alone.
||Sequential: Linear and orderly, learns
in small incremental steps.
||Global: Holistic and system wide thinkers,
learns in large steps.
Table 1: Learning Dimensions and Styles in the Felder-Silverman
Style Model [Sharda,03]
3.1.2 Optimum learning style
We have adopted the Felder-Silverman model for learning style categorization.
Given the variety of learning style theories and models that are available
in the literature (for an overview see [Karagiannidis,
04]), we need to determine which is the most appropriate for our system.
We have chosen the Felder and Silverman model for a number of reasons,
the most important being: first, because this model was specifically conceived
and tested for Engineering Education, the context which <e-aula>
is aiming at.
And second, because the Index of Learning Styles by Felder and Silverman
provides a simple five-way classification mechanism for learning style
estimation based on the administration of a 44 item questionnaire. Therefore,
the assessment mechanism available for the model is quite reasonable in
- Time. As the profile for the learner is determined by the initial questionnaire,
the student has to be able to complete it in a reasonable time and with
- Cost effectiveness. This is one of the basic goals of the whole <e-aula>
In the Felder-Silverman model, learning styles are classified over five
dimensions; each with two options as shown in Table 1.
Most individuals tend to have a dominant learning style in each dimension,
depending upon their personality.
3.1.3 The Learner's background knowledge
<e-aula> classifies learners into five different categories depending
on their previous knowledge:
- Beginner, novice, intermediate, advanced, expert.
The last criterion (learner background) is considered a short-term modelling
characteristic as it attempts to model one aspect that will change during
course interaction. In <e-aula> we use a combination type for this
particular kind of user modelling process (short term modelling). This
means the student is categorized initially by a stereotype and then the
stereotype is gradually complemented as more information is acquired from
the student's interaction with the system.
The first and second criteria (learning objective and optimum learning
style) are considered as long-term modelling aspects and they will be updated
mostly under learner control.
The learner modelling process makes short and long term modelling and
stores this information in a standard compatible way as part of the Learner
Information Package [IMS LIP, 05] record. <e-aula>
maps this information to the data of LOs metadata, resulting in a ranked
list of the most suited learning objects for a specific profile.
3.2 <e-aula> Adaptation Constituents
The <e-aula> adaptation constituents are the learning objects.
The size and scope of each piece of learning content that are combined
to form a personalized course is a key consideration here. Better adaptation
and reuse of learning objects may be obtained using fine-grained learning
objects (a paragraph or a diagram size) as the flexibility for the creation
of additional content packages increases. Nevertheless, the smaller the
objects are, the greater the annotation effort implied.
To cope with our personalization objectives and still maintain a cost
effective system, <e-aula> learning objects are self-contained units
designed to be instructionally independent: this means they aim to teach
a particular concept or ability. We have solved the flexibility-annotation
trade off by defining two levels of adaptation:
3.2.1 Intra learning object level
This is the way the system adapts information to suit the prior knowledge
and the learner's knowledge objectives.
In <e-aula> all the basic course contents are represented in XML
and not in HTML as happens in most e-learning systems [Sancho,
04]. When a specific content is accessed from the webserver an XSL
transformation is applied to the content and an HTML is delivered to the
web client. In addition, XML also gives us the possibility to generate
other output types of lessons such as PDF versions. Additionally, our marked
LOs enable the adaptation of the content itself, using a basic concept
of level of detail.
3.2.2 Inter learning object
This is the adaptation mechanism used to tailor the learning experience
to the learning style that best suits a specific learner.
By re-sequencing the learning content in the package, or by adding additional
content, or by adding content that supports different learning approaches,
the course may be suited to different learning style categories while still
supporting a common learning objective and a learner's initial knowledge
A list with a rank of the most suitable learning objects is obtained
based on the learner's learning style profile and information associated
with the learning object through its metadata record. This information
includes a specific entry associated with the pedagogical ontology to identify
the kind of educational resource most convenient to a specific learning
style and another entry to the concept domain ontology.
<e-aula> learning objects are annotated using a restricted set
of LOM categories which turn out to be sufficient to annotate and query
our resources (see Table 2), plus two additional terms:
one associated with the pedagogical ontology and the other with the concept
domain ontology. The corresponding ontology term is expressed through a
taxon sub element (see Table 2, 9.2.2).
At this stage of the project, we are using the pedagogical classification
proposed by Ullrich [Ullrich, 04] to represent the
instructional features needed to identify the resource for a particular
learning style adaptation. The different classes of this ontology and the
relations among them can be seen in Figure 3. The goal
of this ontology is to provide a description of a learning resource from
an instructional perspective. Each class of the ontology stands for a particular
instructional role the resource can play.
In our personalization architecture, once the learning style type for
a learner has been identified, it will be possible to select the most suitable
learning objects in a certain context domain for that particular learning
style type, according to the ontology classification. For instance, let's
say we have identified an Active learner. In order to compose the course,
learning objects of the class Interactivity (Exploration, Real World Problems,
Invitation....) will automatically be preferred to the ones belonging to
the Example class, which will be more suitable for a Reflective learner.
We use RDF [RDF, 04] as the binding technology
for annotating our resources. Semantic web technologies, particularly RDF,
provide for interesting possibilities. It can be used as a simple ontology
language, as well as for describing the resources according to LOM, using
the RDF bindings of LOM [Nilsson, 2001].
On the other
hand, more powerful ontology languages like OWL [OWL,
04] can be used on top of RDF. OWL also provides for query languages
and reasoning rules, further enriching the possibilities of RDF.
3.3 <e-aula> instructional design for adaptation
The design of a course involves the definition and the classification
of learning goals, the selection of suitable teaching methods and their
assembly into a course [Van Merrinboer, 97]. This
requires a conceptual representation that explicitly models the relationships
among the LOs. In <e-aula> we are using IMS Learning Design specification
to address these issues.
Table 2: <e-aula> application profile
IMS LD states that any instructional design can be represented by the
following conceptual model (classes are in italics) [Koper,
"A person is assigned to a role in the teaching process,
typically a learner or a staff role. In this role he or she
works towards certain outcomes by performing more or less structured
learning and/or support activities within an environment.
Which role gets the activities at what moment in the process, is determined
by learning design method or by a notification (triggering
IMS LD describes how a learning design unfolds through the analogy of
a theatrical play: the play is presented in a series of acts in which people
play roles and undertake a series of activities within the act (for a learner
role this might include discussing a problem with classmates or visiting
an exposition). An act is completed after all the activities of a specified
role or roles, are finished. When one act ends, the next one starts. The
play finishes when all acts are completed.
In the <e-aula> model all possible Learner Profiles are represented
by sub roles of the role learner (we have created 10 different sub roles
to address all the possibilities of <e-aula> adaptation goals: sensing,
intuitive, visual, verbal, inductive, deductive, active, reflective, sequential
and global). Using the learners' classification we can create different
role-parts within an act that fit in with the learners' learning
style, that is to say, we want learners to grasp a learning objective but
we model these role-parts in a different way for each role so that
each learner feels more comfortable.
In the current stage <e-aula> performance is static, which means
course structure is predefined. Our long term goal is to develop ontologies
for different pedagogical approaches depending on the learning style. This
will allow for specific agents to create specific units of learning according
to the principles of a certain pedagogical ontology, including the possibility
of constructing simple units of their own.
4 Related Work
Ontologies have been proposed as a general tool to help to overcome
problems in AI and education [Mizoguchi, 00]. More
specifically, research on learning object technology [Brasse,
03] describes the use of a context domain ontology to enrich LOM with
Figure 3: Pedagogical Ontology [Ullrich,
Dolog [Dolog, 04] proposes an architecture for
distributed e-learning environments based on semantic web technologies.
They describe three different ontologies to undertake the adaptation features:
one for describing learning resources, one for describing domain information
and another for describing learners with respect to user interests, performance,
goals, preferences and so on. None of these represent the instructional
function of a resource.
On the other hand [Ullrich, 04] and [Meisel,
03] propose the use of pedagogical ontologies to capture instructional
design knowledge. Nevertheless, none of these proposals is specifically
oriented to design teaching methods adapted to different learning style
The <e-aula> approach combines these two different kind of proposals:
we use a context ontology and a pedagogical ontology. Both ontologies will
be used to create dynamic personalized courses using IMS LD specification.
5 Conclusions and Future Work
Even though learning standards have reached unquestionable goals in
terms of interoperability, we think more effort is needed in terms of describing
the learning objects context in terms of domain knowledge and the instructional
use of the learning objects. This will enable the development of intelligent
applications capable of adapting learning to individual characteristics
in a dynamic way, and also, to improve reusability of the learning resources
by enriching the possibilities for searching.
In this paper we have proposed a standard compatible way of describing
learning objects based on ontological representations. The knowledge domain
(which for our system is restricted to Programming Languages) can be easily
represented by standard ontologies, like the ones proposed by the ACM Computing
Classification System [ACM, 98]. Nevertheless no proposals
for standards have yet been developed for instructional purposes. We are
working with the pedagogical ontology proposed by [Ullrich,
04] to classify our learning resources from an instructional perspective.
Courses in <e-aula> are designed according to IMS LD in a way
that permits adaptation according to different learning objectives and
The next step in our <e-aula> project will be to extend the pedagogical
ontology to better fit system requirements in terms of the different learning
styles and to enable the dynamic generation of simple units of learning.
The Spanish Committee of Science and Technology (TIC2001-1462 and TIN2004-08367-C02-02)
has supported this work.
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