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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 9, September 2012
Contextual Ontology for Delivering Learning
Material in an Adaptive E-learning System
Kalla. Madhu Sudhana Dr V. Cyril Raj
Research Scholar, Dept of Computer Science Head, Dept of Computer Science
St. Peter’s University Dr M.G.R University
Chennai, India Chennai, India
kallamadhu1@yahoo.com hod-cse@drmgrdu.ac.in
Abstract— The rapid growth of internet technology and the the appropriate presentation method along with the user
explosion of learning material in educational domain are leading preferences.
to the next generation E-learning applications that exploit user
contextual information to provide a richer experience. One of the Here we discuss the general notion of context as well as
activities to perform during the development of these context- how it can be specified and modeled in E-learning domain. The
aware E-learning applications is to define a model to represent architecture of context-aware and adaptive learning system is
and manage context information. In this work, the model for discussed along with the context ontology to model context-
Context-aware and adaptive learning system has been proposed related knowledge.
and introduces context ontology, to model context-related
knowledge that allows the system to deliver learning material by This article is organized as follows. In the second and third
adapting learner context in an adaptive learning system. sections, we study the background concepts and related works
to his paper. In section four the need for the proposed system is
mentioned. In sections five and six, we describe the
Keywords-component; Context aware e-learning; Adaptive architecture of proposed adaptive learning system and the
Delivery of learning material; Ontology based context model ontology based context model for adaptive delivery of learning
material.
II. BACKGROUND
I. INTRODUCTION
A. Context
The explosion of learning material in educational domain Context is a multifaceted concept that has been studied in
are leading to develop E-learning applications, services, agents multiple disciplines, each discipline tends to take its own
and recommender systems appeared to improve the quality of idiosyncratic view that is somewhat different from other
E-learning. Such systems were used in learning systems to disciplines and is more specific than the standard generic
provide the facilities during the learning process and help dictionary definition of context as “conditions or circumstances
learners with a more accurate learning. These forces any E- which affect something” [2].
learning application developed under the ambient intelligence
paradigm to be aware of contextual information and to be able B. Learning Context
to automatically adapt to learner context.
The term learning context is used to describe the current
The development of context-aware E-learning applications situation of a learner related to a learning activity. In addition
should be supported by adequate context modeling and to attributes relying on the physical world model, like time and
reasoning techniques [1]. Modeling context knowledge is a location, a variety of attributes described implicitly or
crucial task to support the delivery of the right information at explicitly might be added to the context. When using an
each moment. The context of the learner and learning appropriate context-modeling technique, the current situation
environment should be extracted for adaptation, personalization might be compared with the requirements of any specific
and anticipation of learning material that is suitable for learner. learning activity.
Current E-Learning solutions are not sufficiently aware of
the context of the learner, that is the individual’s characteristics C. Ontology
and the organizational context such as the work processes and According to Semantic Web led by W3C (World Wide
tasks. The traditional E-Learning systems provide adaption Web Consortium), ontology is a way to describe knowledge
based only on user preference, to improve performance, it is systematically; a typical and explicit specification about
required to incorporate learning environmental context concepts and conceptualization, that is, it also defines concepts
information such as the device or network context to determine
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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 9, September 2012
and relations required to describe meaning and information [3], IV. NEED FOR THE PROPOSED SYSTEM
[4]. The contextually aware environment aims to aid in this
task, presenting the right information to the user. In order to
achieve this, a system must have a thorough understanding of
D. Contextual Ontology its environment, the preferences and devices that exist within it,
the system must be able to identify where, and under what
Ontologies are one of the most functional means for context each person is working.
representing contextual data. They map three basic concepts in
a context model (classes, relationships and attributes) to the Our approach heavily relies on semantic modeling of the
existing things in a domain [5]. The formalism of choice in learner’s environment. For this purpose, we make use of
ontology-based models of context information is typically ontology for modeling contextual knowledge of the learning
OWL-DL [6] or some of its variations, since it is becoming a environment to use them during the context aware adaption
de-facto standard in various application domains, and it is process. The Protégé 4.1 is used to create ontology for
supported by a number of reasoning services. By means of modeling contextual knowledge of the learner’s environment.
OWL-DL it is possible to model a particular domain by
defining classes, properties and relations between individuals. V. PROPOSED SYSTEM
The proposed context-aware and adaptive delivery system
III. RELATED WORKS can be more usefully constructed in a fashion that is tailored
Particularly in mobile and pervasive environments there are specifically to academic e-learning environment for adaptive
different heterogeneous and distributed entities that must delivery of learning material. This may be achieved through
interact for exchanging users’ context information in order to integration of different contextual situations of academic e-
provide adaptive services. To this end, various OWL learning environment.
ontologies have been proposed for representing shared
The context aggregator collects all contextual information
descriptions of context data. Among the most prominent
supplied by different context sources and provides an
proposals are the SOUPA [7] ontology for modelling context in
aggregated knowledge view. The representation and reasoning
pervasive environments, and the CONON [8] ontology for
of contextual information in knowledge base is performed by
smart home environments.
means of Ontology represented in OWL format. The
Schmidt and Winterhalter [9] are using context to retrieve knowledge acquired from the ontological reasoner enables the
relevant learning object for a given user. The matching service system to suggest appropriate learning material to be delivered
computes a similarity measure between the current user context to the learner.
abstraction and the ontological metadata of each learning object
and then can present a ranked list of relevant learning objects. In the proposed system the basic elements of context-aware
It is a kind of active use of context intending to reconfigure and adaptive delivery process is made of three-steps as shown
available services (learning objects). in “Fig. 1”.
Bomsdorf [10] developed a system prototype by allowing
learning materials to be selected depending on a given situation
– this takes into account learner profiles such as their location,
time available for learning, concentration level and frequency
of disruptions.
Bouzeghoub et al. [11] proposed a situation-aware
framework/mechanism which takes into account time, place,
user knowledge, user activity, user environment and device
capacity for adaptation to user.
Lee et al. [12] developed a Java Learning Object Ontology
for an adaptive learning tool to facilitate different learning
strategies/paths for students, which can be chosen dynamically.
Jane Yau and Mike Joy [13] described the architecture of
Context-aware and Adaptive Learning Schedule (CALS) tool. Figure 1. Basic elements of context-aware and adaptive delivery process
This tool is able to automatically determine the contextual
features such as the location and available time. The A. Context Acquisition
appropriate learning materials are selected for the students
according to, firstly, the learner preferences, and secondly the Before modeling the user context model, the most
contextual features. important point in context-aware applications is the acquisition
of context information. There is no single way of determining a
user’s context in E-learning. This mainly depends on the three
strategies that we considered in the proposed system, such as
details of learning device used by learner, what are the basic
47 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 9, September 2012
details of learner? And what are the personal preferences of context model is also a system of concepts (entities) and
learner? Therefore, in the proposed system context information relations, so that the ontology is a possible mean for context
acquisition includes three approaches that allows for plugging modeling to specify the representation of contextual
in different context sources as shown in diagram “Fig. 2”. knowledge. An ontology is “formally defined”, is useful for a
These context perspectives are then integrated into a single computer to interpret it, e.g. for reasoning purposes, and then
context abstraction. Context sources could be: the Rules can be used to implement context reasoning. In the
proposed system ontology is formally represented in the OWL
format.
• Learner profile: First category is the information
obtained from the learner’s profile such as location, C. Adaptation Mechanism
qualification, organization etc. These factors require In E-Learning environments, we may provide Learning
the learners to fill in before they participate in the
contents not only adaptive to learner, but also adaptive to
course.
learning environment. The learning environment may vary
• Context detection service: the information obtained based on learning device, domain, Learner-Preference etc, so
through device context detection service provides the by incorporating the contextual knowledge in adaptive
details about the device being used by learner. mechanism of E-Learning systems will make it more effective.
• User interface: In E-learning domain different users
The adaptive process based on context creates suitable
may prefer different orientation of learning, learning
mode and subject area and so on, once the basic content for learners according to contextual and situational
material provided to the learner the user interface data. Secondly, content adaptation process recodes original
provides environment to obtain the personal content into adapted contents according to the adaptive
preferences of the user based on which the system will suggestion, from adaptive process. The proposed Context-
deliver the preferred material. aware adaptive content delivery model is as shown in “Fig. 3”.
Figure 2. “Context Acquisition- Modeling- Adaptation” Scenario in
Adaptive System
B. Context Modeling
Figure 3. Proposed context ontology based Learning content delivery model
In general, the context data may be from learner,
learning environment, educational strategy and so on. The
specification of all Contextual entities and relations between
these entities are needed to describe the context as a whole. A
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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 9, September 2012
VI. PROPOSED ONTOLOGY CONTEXT MODEL A. Learner-Context class
We used an ontology-based context model for context This context class is the super class for all the contexts in
representation. This model adopts OWL as the representation Context Aware Learning environment. Any instance of the
language to enable expressive context description and data context class represents a conceptual context. Different
interoperability with third-party services and applications and it contexts can be indexed hierarchically based on class
is a W3C recommendation that employs web standards for hierarchy, such as Personal, Device and Preference as shown in
information representation such as RDF and XML Schema. “Fig. 5”.
Because context ontologies have explicit representations of
semantics, they can be reasoned by the available logic
inference engines. Systems with the ability to reason about
context can detect and resolve inconsistent context knowledge
that often results from imperfect sensing.
Here, we consider three categories of contextual
information for the proposed system that are mentioned below,
are mainly important and especially concerned to an adaptive
E-learning systems based on which the proposed system can
deliver the concerned learning material to the learner.
Our ontology context model, which is a context aware
learning environment made by OWL. It consists of three top-
level classes and twelve sub-classes, and contains fifteen main
properties which describe the relations between individuals in
top level class and its sub classes. “Fig. 4” shows that we
comply with XML, RDF Schema and OWL as a part of the
context model and give a definition of three top level classes.
<rdf:RDF
xmlns:owl ="http://www.w3.org/2002/07/owl#"
xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xmlns:xsd ="http://www.w3.org/2001/XLMSchema#">
<owl:Ontology rdf:about="">
<rdfs:comment>Learner OWL ontology</rdfs:comment>
<rdfs:label>Learner Context Ontology</rdfs:label>
</owl:Ontology>
<owl:Class rdf:ID="Personal">
<rdfs:subClassOf rdf:resource="#Learner-Context"/>
</owl:Class>
<owl:Class rdf:ID="Device">
<rdfs:subClassOf rdf:resource="#Learner-Context"/>
</owl:Class> Figure 5. Classes and subclasses relationships in context ontology
<owl:Class rdf:ID="Preference">
<rdfs:subClassOf rdf:resource="#Learner-Context"/> OWL defines the vocabulary of context model. It provides
a mechanism to define adaptive -specific properties and classes
</owl:Class>
of context to which those properties can be applied, using a set
-------------
of basic modeling primitives (class, subclass, properties,
------------- domain, range, type). The context model can be specified using
------------- OWL encoding, Fig. 6(a) and, Fig. 6(b) shows that each
</rdf:RDF> statement is essentially a relation between an object (a class),
Figure 4. A part of ontology expressions in context model an attribute (a property), and a value (a resource or free text).
49 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 9, September 2012
Fig. 6(c) shows an example OWL coding part for small part of <owl:DatatypeProperty rdf:about="ID">
our proposed ontology. <rdfs:domain rdf:resource="#Identity"/>
<rdfs:range
Classes Object Data type Value Type rdf:resource="http://www.w3.org/2001/XMLSchema#string"/>
Property Property
</owl:DatatypeProperty>
Identity hasPersonalInfo ID Xsd: string
Personal Username <owl:DatatypeProperty rdf:about="UserName">
Learner-Context hasIdentity Password
(a) <rdfs:domain rdf:resource="#Identity"/>
<rdfs:range
rdf:resource="http://www.w3.org/2001/XMLSchema#string"/>
</owl:DatatypeProperty>
<owl:DatatypeProperty rdf:about="Password">
<rdfs:domain rdf:resource="#Identity"/>
<rdfs:range
rdf:resource="http://www.w3.org/2001/XMLSchema#string"/>
</owl:DatatypeProperty>
</rdf:RDF>
(c)
Figure 6. (a) Few specifications of model, (b) The equivalent directed
semantic graph, and (c) An example of OWL code.
(b)
<rdf:RDF • Personal: This ontology classes contains a wide
xmlns:owl ="http://www.w3.org/2002/07/owl#"
categorization details provided by the learner in
Learner Profile. It was created in order to facilitate the
xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#" extraction of the user personal information. The user is
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" requested to register and fill information in few forms
with personal information.
xmlns:xsd ="http://www.w3.org/2001/XLMSchema#">
1. Identity (e.g.: ID, Name or Registration-Number)
<owl:Ontology rdf:about="">
2.Organization(e.g.: Technical Institute, University or
<rdfs:comment>Learner OWL ontology</rdfs:comment> Research Organization)
<rdfs:label>Learner Context Ontology</rdfs:label> 3. Location (e.g.: City, State or Country name)
4. Role (e.g.: Student, Lecturer or Professor)
</owl:Ontology> 5. Goal (e.g.: Research, Survey, Quick Reference, Basic
<owl:Class rdf:ID="Identity"> Introduction or Seminar)
6. Grade (e.g.: Beginner, Practitioner or Expert)
<rdfs:subClassOf rdf:resource="#Personal"/>
7. Qualification (e.g.: Bachelor, Master or Researcher)
</owl:Class> 8. Domain (e.g.: Computer Science, Agriculture etc)
<owl:Class rdf:ID="Personal">
<rdfs:subClassOf rdf:resource="#Learner-Context"/> • Device: To be able to cover the device and software
heterogeneities in a learning environment, we have
</owl:Class>
included device context along with its sub-classes such
<owl:ObjectProperty rdf:ID="hasIdentity"> as Hardware, Software and Network-Connectivity. It
models knowledge about the different devices that are
<rdfs:domain rdf:resource="#Personal"/>
being used by learner.
<rdfs:range rdf:resource="#Identity"/>
1. Hardware (e.g.: Mobile, PC, Laptop or PDA)
</owl:ObjectProperty> 2. Software (e.g.: Operating system, browser or audio and
<owl:ObjectProperty rdf:ID="hasPersonalInfo"> video encoding software)
3. Network-Connectivity (e.g.: Wired or Wireless)
<rdfs:domain rdf:resource="#Learner-Context"/>
<rdfs:range rdf:resource="#Personal"/>
• Preference: In e-learning environment the category of
</owl:ObjectProperty> learning material is an important context based on
50 http://sites.google.com/site/ijcsis/
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 10, No. 9, September 2012
needs and interests under the context of [9] Schmidt A., C. Winterhalter (2004) “User Context Aware Delivery of E-
personalization. The Preferences of learner is useful to Learning Material: Approach and Architecture”, Journal of Universal
Computer Science (JUCS), Vol. 10(1) pp. 28-36.
select and deliver the suitable type of material based on
[10] Bomsdorf, B. (2005) Adaptation of Learning Spaces: Supporting
Subject-Area, Mode-of-Learning (material format) and Ubiquitous Learning in Higher Distance Education, Dagstuhl Seminar
Learning Orientation. The user is requested to enter Proceedings 05181: Mobile Computing and Ambient Intelligence: The
this information while interacting for learning material. Challenge of Multimedia.
[11] Bouzeghoub, A.Do, K. and Lecocq, C. (2007) Contextual Adaptation of
1. Subject-Area (e.g.: Data-Structure, Embedded Systems, Learning Resources, IADIS International Conference Mobile Learning,
neurology or Dental) pp. 41-48.
2. Mode-of-Learning (e.g.: Video, audio, textual or [12] Lee, M., Ye, D. and Wang, T. (2005) Java Learning Object Ontology,
animation) International Conference on Advanced Learning Technologies, pp. 538-
542.
3. Orientation-of-Learning (e.g.: Case-Study, Example
[13] Jane Yau and Mike Joy. (ICALT 2007) Architecture of a Context-aware
Oriented, problem-oriented or conceptual) and Adaptive Learning Schedule for Learning Java.
AUTHORS PROFILE
VII. CONCLUSION AND FUTURE WORK
We have described our proposed model for Context-aware
and Adaptive Learning system and introduced context ontology Dr. V. Cyril Raj received
for E-Learning, to deliver learning material by adapting learner Bachelor degree in Electronics
context and we are currently designing the system prototype and Communication, Master
which will be implemented and evaluated. To evaluate the degree in Computer Science and
system a small number of students will be employed to work Engineering and PhD from
with the system and to provide us with qualitative results. Jadavpur University. He is
currently Head of the Department
of Computer Science and
We believe that the primary advantages of our otology- Engineering, Dr. MGR
based context model, contains a hierarchical content structure University, Chennai, India. He
and semantic relationships between concepts. It can provide has published number of papers in national and
related and useful semantic based context information for international conferences, seminars and journals and
searching learning material in context-based e-learning
author of many text books. At present many members are
environment.
doing research work under his guidance in different areas.
His research interests include Bioinformatics, Semantic-
VIII. REFERENCES Web, Computer Networks and Data Mining.
[1] M Poveda-Villalon, M C Suárez-Figueroa, R García-Castro. (2010) A
Context Ontology for Mobile Environments- oa.upm.es-2010.
[2] Webster, N., (1980) Webster’s new twentieth century dictionary of the
English language. Springfield, MA: Merriam-Webster, Inc.
[3] T. Berners-Lee, J. Hendler, and O. Lassila, (2001) “The Semantic Web,”
Scientific American, May:17 2001, pp. 28-37.
[4] T. Gruber, (1995) “Toward Principals for the Design of Ontologies Used
for Knowledge Sharing,” International Journal of Human-Computer
Studies, Vol. 43. Kalla.Madhu Sudhana received
[5] De Almeida, et al. (2006) Using Ontologies in Context-Aware Bachelor degree in Computer
Applications. Proc. of Database and Expert Systems, Poland. Science and Engineering from
[6] Horrocks, P. F. Patel-Schneider, F. van Harmelen. (2003) From SHIQ Visvesvaraya Technological
and RDF to OWL: The making of a web ontology language, Journal of University, Bangalore and Master
Web Semantics 1 (1) 7–26.
degree in Computer Science and
[7] H. Chen, F. Perich, T. W. Finin, A. Joshi. (2004) SOUPA: Standard
Ontology for Ubiquitous and Pervasive Applications, in: 1st Annual
Engineering from Dr. MGR University, Chennai. He
International Conference on Mobile and Ubiquitous Systems, IEEE worked as Assistant Professor in many Engineering
Computer Society, 2004. Colleges. Currently he is a research scholar in Department
[8] D. Zhang, T. Gu, X.Wang. (2005) Enabling Context-aware Smart Home of Computer Science and Engineering, St. Peter's
with Semantic Technology, International Journal of Human-friendly University, Chennai, India. His research interests are
Welfare Robotic Systems 6 (4), pp. 12–20.
Ontology, Semantic-Web and E-learning.
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