Isabela Gasparini
, Ana Marilza Pernas
, Amel Bouzeghoub
José Palazzo M. de Oliveira
, José Valdeni de Lima
and Marcelo S. Pimenta
Instituto de Informática, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
Universidade do Estado de Santa Catarina, Joinville, Brazil
Universidade Federal de Pelotas, Pelotas, Brazil
TELECOM & Management SudParis, Evry, France
Keywords: Adaptation/Personalization, Cultural and context-aware, Situation-aware, e-Learning environments.
Abstract: Although there are several approaches for adaptive e-learning systems, they focus mainly on technological
and/or networking aspects without taking into account other contextual aspects, such as cultural and peda-
gogical context. This paper presents a context-aware situation-dependent personalization approach designed
for an adaptive e-learning system called AdaptWeb
, based on a rich context model as an extension to stu-
dent modeling.
A Web-based e-learning system (ELS) is normally
used by a wide variety of students. Adaptive ELS
adjust the content, presentation and navigation to a
student's model. Personalization (or adaptation) is
the process of adapting a computer application to the
needs of specific user and takes advantage of the
acquired knowledge about him/her. ISO defines
usability as evaluated by the effectiveness,
efficiency and satisfaction which users achieve
specified goals in a particular context of use (ISO
9241, 1998). Specifically in an e-learning
environment: i) an user is typically someone
playing a student or a teacher role; ii) goals are
related to activities of learning process, by acquiring
new knowledge, behaviors, skills, preferences or
values, and may involve synthesizing different types
of information; and iii) differently from human-to-
human interactions, in human-to-computer
interactions, the context of use is usually described
by a set of (cultural, technological, pedagogical, etc.)
characteristics that are necessary to support the
learning process and its goals.
Since ELS are normally used by a wide variety
of students with different skills, background,
preferences, and learning styles, an ELS must
provide improved usability being adaptive and
personalized. Traditionally, the most widely used
components of student profiles have been considered
(Brusilovsky and Míllan, 2007): knowledge,
individual traits such as learning or cognitive styles,
experiences and background (Souto, 2002), goals or
tasks, as hierarchical task network planning
presented in Ullrich (2008). However, there are
other relevant criteria to reach personalization, such
as motivation, working memory capacity,
personality traits, behavior, culture, etc. (BROWN et
al. 2009). At the same time, ELSs may be
dynamically adjusted not only according to the
student’s model but also depending on a richer
notion of context. A contextualized ELS provides
the student with the material he needs, and
appropriate to his knowledge level and which makes
sense in a special learning situation, called a
scenario (Eyharabide et al., 2009), (Gasparini 2010).
The aim of our research is to investigate
approaches putting the users’ profile and contextual
information into practice in the development process
of the ELSs AdaptWeb
(Freitas et al. 2002), whose
goal is to adapt content, presentation and navigation
in an educational web course according to the
student model. AdaptWeb
is an open source
environment, available in SourceForge
This paper is organized as follows. Section 1
presents an overview of our research and objectives.
Section 2 explains some related works. Section 3
discusses our approach to modeling context and
Gasparini I., Marilza Pernas A., Bouzeghoub A., Palazzo M. de Oliveira J., Valdeni de Lima J. and S. Pimenta M..
DOI: 10.5220/0003291001650172
In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU-2011), pages 165-172
ISBN: 978-989-8425-49-2
2011 SCITEPRESS (Science and Technology Publications, Lda.)
culture. Section 4 introduces our extended
architecture. Section 5 illustrates some examples of
utilization. Finally, in section 6 we expose our
Research in adaptive educational hypermedia has
demonstrated that considering context leads to a
better understanding and personalization (Brusi-
lovsky and Míllan, 2007). Context is vital to im-
prove personalization in ELSs. Recent works aim to
provide the capacity for identifying the right con-
tents, right services in the right place at the right
time and in the right form based on the current stu-
dent´s situation. There is an interesting theory of
learning for a mobile society (Sharples et al., 2007)
but our work is closely related to others like (Bar-
bosa et al., 2006), (Yang et al. 2006), (MOBIlearn,
2003) and (Bouzeghoub, and Do Ngoc, 2008). The
interesting propositions of GlobalEdu (Barbosa et
al., 2006) in terms of architecture, for instance, have
distributed and central alternatives with different
models (student, context and environment).
An infrastructure to ubiquitous learning is pre-
sented in Tetchueng et al. (2007) where an environ-
ment to provide collaborative learning is proposed,
based on three systems: a peer-to-peer content
access and adaptation system; a personalized annota-
tion management system and a multimedia real-time
group discussion system.
Particularly about cultural aspects, Blanchard
and Mizoguchi (2008) describe an upper ontology of
culture, by working at the meta-level of culture.
They aim to identifying major constituents to be
considered when dealing with any kind of cultural
issue without having to endorse a particular culture’s
representational framework. They use this approach
to deal with many CATS (Culturally-Aware Tutor-
ing Systems) related issues by providing objective
formalism for cultural representation. Chandramouli
et al. (2008) presented the notion of the CAE-L On-
tology for modeling stereotype cultural artifacts in
adaptive education and used a Cultural Artifacts in
Education (CAE) questionnaire to gather the infor-
mation required to determine if there is a significant
cultural bias within online education, specifically
Adaptive Educational Hypermedia.
Motz et al. (2005) introduce an architecture in
the e-learning EduCa Project, based in a strong use
of ontologies for the retrieval, management and clus-
tered of electronic educational resources according
to user’s cultural aspects, like degree of impatience,
attitude, treatment, language, learning styles and
activities. These cultural aspects are specified in a
MultiCultural Aspects Ontology, which follows the
standard Learning Object Metadata (LOM) and uses
OWL (Web Ontology Language). Sieg et al. (2007)
presented a framework that integrates critical ele-
ments that make up the user context, namely the
user’s short-term behavior, semantic knowledge
from ontologies that provide explicit representations
of the domain of interest, and long-term user profiles
revealing interests and trends. They present a novel
approach for building ontological user profiles by
assigning interest scores to existing concepts in a
domain ontology.
Reinecke et al., (2007) present a Cultural User
Model Ontology (CUMO) that contains information
such as different places of residence, the parents’
nationality, languages spoken, and religion. Fur-
thermore, CUMO contains information about Hofs-
tede’s (Hofstede, 1991) national cultural dimensions.
Our research has a different point of view of
these works as we integrate cultural, technological,
pedagogical and personal aspects as part of a rich
context model (Eyharabide et al., 2009) that makes
sense in a special situation, in a given time. An im-
provement in the user’s contextual information leads
to a better understanding of users’ behavior in order
to adapt (i) the content, (ii) the interface, and (iii) the
assistance offered to users. A contextualized ELS
provides the student with exactly the material he
needs, and appropriate to his knowledge level and
that makes sense in a special learning situation.
However, while learning is a process intensively
related to the notion of situation, in most of ELSs
situation is only implicitly mentioned and not expli-
citly modeled. In order to support situation-aware
adaptation, it is necessary to model and specify both
context and situation. More accurately, there is a
complex intermeshing and continuous transforma-
tion of situations in combination with fluctuating
contexts, where meaning changes according to con-
text and through preferences of different partici-
pants. In this sense, e-learning personalization is
situation-dependent and cannot be managed in an
independent form.
In this section, our approach to model context and
culture in e-learning is described. Specially, we im-
proved the models used in the AdaptWeb
CSEDU 2011 - 3rd International Conference on Computer Supported Education
ment in order to incorporate the notion of context
and situation. We add a rich notion of context to
existing student profiles in order to provide a rich
personalization process. To be effective, learning
process must be adapted not only to the student’s
profile but to the learner’s context as well, creating
some kind of matching between context and profile
to provide for example the appropriate content, na-
vigation, and recommendations. Learning processes
have to provide extremely contextualized content
that is highly coupled with context information, li-
miting their reuse in some other context. If the con-
text information is represented independently from
content information, the possibilities for reuse in-
In a broader sense, context describes the circums-
tances under which something occurs as well as the
interrelationships of those circumstances. Such inter-
relationships provide a semantic perspective that
restricts and narrows the meaning of “something”
(Abarca et al., 2006). A context-aware ELS is an
application that adapts its behavior according to the
students’ context. Context-aware applications not
only use context information to react to a user’s re-
quest, but also take the initiative as a result of con-
text reasoning activities (Dockhorn Costa et al.,
2007). We have developed a model based on upper-
level ontology. In this model, a student might be
involved in several overlapping contexts, and conse-
quently, his/her educational activity might be influ-
enced by the interactions between these contexts.
Overlapping contexts contribute to and influence the
interactions and experiences that people have when
performing certain activities (Bouzeghoub and Do
Ngoc, 2008), (Yang et al., 2006), (Eyharabide and
Amandi, 2008). Our model has three levels: meta-
model, model (ontologies), and object (Eyharabide
et al. 2009). The meta-model level is represented by
an upper ontology; the model level with several on-
tologies to describe the elements that populate the
context and, in the lower level, we find the instantia-
tions of the context ontologies. In other words, the
ontology concepts of one level are the instantiations
of its immediate superior level.
We personalize an ELS for each user based on
the information stored in a student model. In our
work, the typical characteristics of students are ex-
tended to include the context dimensions having
personal, technological, pedagogical and cultural
Personal context is widely considered in ELS,
usually gathered in user profiles. It considers the
student’s personal information (such as name or ad-
dress) and also the student’s personal preferences
(like interaction preferences, colors or layouts). In
our environment, typical characteristics of user pro-
files include age, scholarship, background, gender,
interests, knowledge, experiences, goals, behavior,
and navigational preferences.
Technological context is related to many differ-
ent technological constraints (e.g., device processing
power, display ability, network bandwidth, connec-
tivity options, location and time). It includes con-
cepts such as browser type and version, operating
system, IP address, devices, processing power, dis-
play ability, network bandwidth or connectivity op-
Pedagogical context consists of multifaceted
knowledge due to many distinct viewpoints of peda-
gogical information needed to personalize e-
learning. In practice, many adaptive systems take
advantage of users’ knowledge of the subject being
taught or the domain represented in the hyperspace,
and the knowledge is frequently the only user fea-
ture being modeled (Brusilovsky and Míllan, 2007).
Recently, various researches started using different
characteristics described in other related fields, such
as personality model OCEAN (Goldberg, 1993),
cognitive (Ford and Chen, 2000) and learning styles
(for example, from Felder's model (Felder and Brent,
3.1 Cultural Context
Cultural context is referred to different languages,
values, norms, gender, social or ethnic aspects or
even ideological, political and religious aspects. It
describes cultural characteristics on different levels,
such as national, organizational or individual charac-
teristics. In turn, culture can be analyzed in some
levels: national and regional aspects, organizational
aspects, professional aspects and fields, and individ-
ual aspects. There are different cultural dimensions
proposed in the literature, but the most accepted for
national point of view are the five dimensions pro-
posed by Hofstede (1991), based on value orienta-
tions and shared across cultures. According to Bos-
sard (2008) there are two categories of topics that
are affected in human computer interaction localiza-
tion, (i) presentation of information (e.g. time, date
and color format) and language (e.g. font, writing
direction, etc.); and (ii) dialog design (e.g. menu
structure and complexity, layout, positions) and inte-
raction design (e.g. navigation concept, interaction
path, interaction speed, system structure, etc.). De-
spite some HCI works now focusing on cross-
cultural aspects in HCI, the research of cultural-
dependent aspects of HCI, is still embrionary (Za-
harias, 2008).
Cultural aspects are preferences and ways of be-
havior determined by the person’s culture. Cultural
context includes cultural background of a student
and may have a great impact on their ability and
efficiency to learn a given set of content (Chandra-
mouli et al., 2008). A Culture Profile cannot be de-
fined as a fixed or prescribed specification. The spe-
cification should be extended and dynamically im-
proved based on the user´s context. As described in
Reinecke and Bernstein, (2007), research conducted
on the effect and usability of culturally adapted web
sites and interfaces has shown enormous improve-
ments in working efficiency.
Cultural-aware in this paper deals in identify na-
vigations paths and user´s behavior in an ELS to
support adapting content and navigation. It is not our
goal to treat sociological aspects.
3.2 Modeling Context and Culture
The meta-model, presented in Figure 1, is an upper-
level ontology describing abstract concepts like user,
application, user profile, situation or date. The mod-
el depicts the different contextual dimensions. Each
contextual dimension is represented by a different
ontology such as a cultural ontology (with concepts
like nationality and values, language, etc.), educa-
tion ontology (course, learning style, discipline,
etc.), personal ontology (name, gender, preferences
as navigational mode and interface colors, birthday,
action focus in this moment, etc.) or technological
ontology (operating system, browser, network
bandwidth, etc.). Finally, the object model will com-
prise instances describing the context of a particular
user like a concrete name (John Smith), a course
(Human Computer Interaction) or a particular lan-
guage (English).
The situation could be completely changed if the
contexts of student change. Among all the possible
information gathered in the student model, we are
especially interested in modeling scenarios because
they change according to context. Scenarios may
depend on the situation the student is now in and on
external factors. The concreteness of scenarios helps
students and teachers to develop a shared under-
standing of the proposed contextual information, and
allows assimilating and representing complex idio-
syncrasies of that they would otherwise misunders-
We define a scenario as a tuple containing an
entity that the student prefers in a given situation, a
relevance denoting the student’s preference for that
Figure 1: Example of a scenario-oriented situation.
entity, a certainty representing how sure we are
about the student having that preference and a date
to indicate when that preference is stored:
Scenario = {entity, situation, relevance, certainty, date}
Situations are the key to include temporal aspects
of context in a comprehensive ontology for context
modeling, since they can be related to suitable no-
tions of time (Dockhorn Costa et al., 2006). As con-
text varies during certain time intervals, it is vital to
consider it within the concept of Situation. Examples
of situations could be “John was at home using his
notebook to read lesson number 3 of the Human
Computer Interaction course” or “A Japanese Pro-
fessor, who speaks English, is adding new exercises
to the course Introduction to Java using a high speed
connection while she travels by train”. Therefore, we
define situation as a set of contextual information in
a particular period of time:
Situation = {Context, initial time, final time}
An example of contextual information would be:
“The student named John is reading lesson number
7”. This is a description relating an entity (the stu-
dent John) to another entity (the lesson number 7)
via a property (is reading). We represent this contex-
tual information as (Student.john, isReading, Les-
son.lesson#7). We define the context as a set of
triples composed by concepts, instances and rela-
tions between them. It is important to emphasize that
the concepts and instances might belong to the same
ontology or different context ontologies:
Context = {(Ca1.Ia1, R1, Cb1.Ib1), ..., (CaN.IaN, RN,
CbN.IbN)}; (C: concept, I: instance and R: relation)
To clarify these ideas, let us consider again John
example. John prefers reading visual learning ma-
terial in a situation when he is at home using his
CSEDU 2011 - 3rd International Conference on Computer Supported Education
notebook to read lesson number 3 of the Human
Computer Interaction course. Hence, the correspond-
ing context1 will be:
Context1={ (Person.John, locatedIn, Location.home), (Per-
son.John, uses, Device.notebook), (Person.john, reads, Les-
son.lesson#3), (Lesson.lesson#3, belongsTo, Course.HCI)}
Situation1={ Context1, 4:00PM, 7:00PM}
Scenario1={User, Situation1, relevance.high, certainty.95%,
The extended AdaptWeb
architecture is presented
in the Figure 2, where the boxes represent the new
modules inserted in the already functioning architec-
ture of AdaptWeb
. Beyond the new modules, is
showed in the figure the three servers which were
proposed to store and model the context data.
Figure 2: Extended architecture.
Starting with the modules, the User Interface
Component is responsible to both obtain the user
data, and present the adaptations processed by the
environment. Actually, the AdaptWeb
already stores all the information related with the
login, the chosen discipline and the author notifica-
tions to the students. So, it is possible to aggregate
user context data to be obtained via interface, like
the learning object actually in use and the path made
by the student while using AdaptWeb
. Knowing
this path, we can discover the occurrence of learning
events that are important to starts an adaptation.
These events are detected by the Context Collec-
tor/Detector and, depending on the event, notified to
the Context Management Service.
The extended architecture is based on three serv-
ers that operate together to provide and manage con-
textualized data according to the student's scenarios.
Each server manages specific data related to the user
context, being respectively responsible for the sto-
rage and adaptation of (i) environmental context
(information related to the user environment, tasks,
activities, time interval, devices, location), (ii) in-
formation about students (personal data, preferences,
objectives, knowledge background, behavior, learn-
ing styles, cultural context, etc.), and (iii) learning
object’s information (documents provided by the
educational environment to its users for their learn-
The Context Management Service is responsible
for analyzing the context managed by the servers,
generating different scenarios that can be expe-
rienced by the students in a specific period. These
scenarios are used to guide the adaptation (in the
Adaptation Engine), and materialized in the interface
rendered to the user. The main goals of the architec-
ture are: (i) easily reuse of educational resources,
since they will be adapted to the user scenario while
the stored content remains the same, (ii) integration
into the existing architecture, since the new architec-
ture is supposed to take advantage of the existing
functionalities and (iii) extensibility to other educa-
tional systems, using standard technologies. The
personalization is possible with the combination of
contextual data related to whom and where the user
is, what he/she is doing and what does he/she needs
to achieve his/her educational targets. More details
can be found in (PERNAS et al., 2010).
In this section, we describe some improvements of
the personalization’s capabilities of AdaptWeb
order to provide support to this contextual modeling
approach. We start by describing different learning
situations to explain the contextual adaptations de-
veloped in AdaptWeb
, and then we detail how
those situations trigger the corresponding contextual
adaptation. We show some examples of possible
contexts in a Database System course.
We show some examples in a Database Systems
course context where the teacher provided a set of
links learning objects with diverse content about
database system, for example: History and motiva-
tion for database systems, Components, DBMS
functions, Database architecture and data indepen-
dence, etc. A learning object (LO) is defined as any
entity, digital or non-digital, that may be used for
learning, education or training (IEEE, 2002). For a
simplification purpose, we have a few variables over
student´s model: student's knowledge, subject, net-
work connection, learning style, Language, Langua-
geLevel and Country.
In Context1, Mike is a student who lives in Bra-
zil, his mother tongue is Portuguese, and he has a
low level knowledge in English. He is trying to learn
about the subject XML databases, which is ex-
plained in English. He is doing exercises about that
subject, but unfortunately he is not obtaining satis-
factory results. In addition, he has a high network
connection and according to Felder’s model (Felder
and Brent, 2005) he is active.
The user model checks his number of mistakes
and identifies if he needs help resolving the exercis-
es. In the meantime, the situation model detects via
teacher’s agenda that a chat with the students was
previously scheduled by the teacher to happen in 15
minutes. These events will start a service of notifica-
tion in the Context Management Service, informing
that a change of the current scenarios related with
these events may change. After a new orchestration
by the Context Management Service, the User Inter-
face sends a message to the student, notifying him of
this possibility to solve his doubts and shows the
“chat” link in a different and highlighted color.
In another scenario, Context2, Marie, a French
speaking PhD student of Engineering from France,
has very good skills in three different foreign lan-
guages (English, Portuguese and Spanish). She is
also learning the subject XML databases and not
having good results. She has a low network connec-
tion and her Felder's learning style is reflective. In
consequence, AdaptWeb
sends a message by email
to her teacher advising to contact the student and
changes the order of the links, putting links related
to video material with low quality resolution in the
end and disabling links related to video material
with high quality resolution (those who are heavy
and difficult to see). Furthermore, AdaptWeb
tects some important links for learning material writ-
ten in English and Spanish and shows this in the top
of the list.
These contexts are formalized as following:
Context1 = {
(Student.Mike, isLearning, Subject. XML databases),
(Subject. XML databases, isExplainedIn, Language.english),
(Student. Mike, hasUserKnowledge, UserKnowledge.bad),
(Student. Mike, hasConnection, NetworkConnection.high),
(Student. Mike, hasStyle, LearningStyle.active),
(Student. Mike, hasMotherTongue, Language.portuguese),
(Student. Mike, hasLanguageSkill, Language.english),
(Student. Mike, hasEnglishLanguageLevel,
(Student. Mike, isCitizenOf, Country.Brazil)}
Context2 = {
(Student.Marie, isLearning, Subject. XML databases),
(Student. Marie, hasUserKnowledge, UserKnowledge.bad),
(Student. Marie, hasConnection, NetworkConnection.low),
(Student. Marie, hasStyle, LearningStyle.reflective),
(Student. Marie, hasMotherTongue, Language.french),
Marie, hasLanguageSkill, Language.english),
(Student. Marie, hasEnglishLanguageLevel,
(Student. Marie, hasLanguageSkill, Language.portuguese),
(Student. Marie, hasPortugueseLanguageLevel,
(Student. Marie, hasLanguageSkill, Language.spanish),
(Student. Marie, hasSpanishLanguageLevel,
(Student. Marie, isCitizenOf, Country.France)}
In summary, the adaptation mechanisms in
can be for example the following ac-
“send notification to student only in
Portuguese” + “show highlighted links”+ recom-
mend LO and content about the same subject (same
concept in the domain ontology) in Portuguese with
a low level of difficulties; An example of this adapta-
tion is presented in figure 3, where the set of LO is
especially adapted to him.
“order links” + “hide or disable links”
+ “show highlighted links” + “recommend LO and
content about the same subject written in French,
English, Spanish or Portuguese”. An example of
this adaptation is presented in figure 4.
Figure 3: LO adapted to Mike´s situation.
Figure 4: LO adapted to Marie´s situation.
CSEDU 2011 - 3rd International Conference on Computer Supported Education
These contexts are used by the context manage-
ment service within logical rules in order to predict
future recommendations.
Currently, this model is being under evaluation
with real students and actual courses in AdaptWeb
The constant evolution of ELSs and the way people
communicates demands the development of new
efficient solutions to offer faster and better informa-
tion, aiming to improve their utilization and, more
ambitiously, the learning process. One meaningful
example is the need to deal with context modeling
and its relation with user modeling. In fact, context
modeling extends traditional user modeling tech-
niques, by explicitly dealing with aspects we sup-
pose to have a significant influence on the learning
process assisted by an ELS.
In this paper, we proposed to improve an ELS
taking into account a richer notion of context, guided
by scenarios, and showing how this context data can
be related in a daily application.
In this work, we look to increase even more the
capabilities of the actual systems personalization,
making use of ontologies to model the student’s con-
text in different scenarios, adapting the systems con-
tent, navigation and presentation. To show a mate-
rialization of this proposition, we extend an existent
ELS environment architecture and explain its new
Research in education has shown that environ-
ments, instructional design and learning methodolo-
gies cannot be always displayed in the same way,
and cannot be universally applied because their ef-
fect can vary from one culture to another, i.e. some
tactics may be effective in a cultural group, but not
in another. As well as other software applications,
ELSs are usually restricted to one personalization
strategy per country. However, a predefined loca-
lized personalization cannot be assigned to all
people of a nation, as some might have many cultur-
al influences and are, therefore, culturally ambi-
guous (Reinecke and Bernstein, 2008). In this way,
researchers are focusing on cultural aspects to pro-
duce learning technologies, and to understand the
dimension of a cultural background has on the
choice of underlying teaching methodologies. In our
approach, we consider culture as another contextual
dimension. Our future work includes increasing cul-
tural dimensions (national, social and personal) as
part of cultural context and for this improvement to
use different aspects as values, norms, social or eth-
nic aspects in the cultural context model.
We expect to have a more understanding of users
and a better communication with LO provided by
teacher, meaning effective learning for students in
environment. We plan to carry out more
experimental tests, with our partners in France and
Argentine, to develop a course with the same content
to a wide variety of students, belonging to different
This work was partially supported by CNPq, Brazil,
(CT-Info/CNPq 17/2007) and by the projects Adapt-
SUR 022/07 (CAPES, Brazil) and AdContext 547-
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