Contextual Ontology for Delivering Learning Material in an Adaptive E-learning System by ijcsiseditor


									                                                           (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

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

                                                                                                   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

                                                                                                         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

                                                                                                          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

   •    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
                                                                                   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

                                                                                                                  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.
xmlns:owl =""
xmlns:rdf =""
xmlns:xsd ="">
<owl:Ontology rdf:about="">
<rdfs:comment>Learner OWL ontology</rdfs:comment>
<rdfs:label>Learner Context Ontology</rdfs:label>
<owl:Class rdf:ID="Personal">
          <rdfs:subClassOf rdf:resource="#Learner-Context"/>
<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
                                                                          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).

                                                                                                           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"/>
      Classes         Object         Data type        Value Type                rdf:resource=""/>
                     Property        Property
Identity          hasPersonalInfo   ID              Xsd: string
Personal                            Username                                    <owl:DatatypeProperty rdf:about="UserName">
Learner-Context   hasIdentity       Password
(a)                                                                             <rdfs:domain rdf:resource="#Identity"/>
                                                                                <owl:DatatypeProperty rdf:about="Password">
                                                                                <rdfs:domain rdf:resource="#Identity"/>

                                                                                Figure 6. (a) Few specifications of model, (b) The equivalent directed
                                                                                semantic graph, and (c) An example of OWL code.
<rdf:RDF                                                                              •   Personal: This ontology classes contains a wide
xmlns:owl =""
                                                                                          categorization details provided by the learner in
                                                                                          Learner Profile. It was created in order to facilitate the
xmlns:rdf =""                                  extraction of the user personal information. The user is
xmlns:rdfs=""                                        requested to register and fill information in few forms
                                                                                          with personal information.
xmlns:xsd ="">
                                                                                      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
                                                                                          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

                                                                                                              ISSN 1947-5500
                                                                   (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-
      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
    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
                                                                                           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-
[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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