PEDAGOGICAL RESOURCES REPRESENTATION IN RESPECT
IN ONTOLOGY AND COURSE SECTION
Patitta Suksomboon, Daniele Hérin
Laboratory of Computer science, Robotics and Microelectronics of Montpellier
Université Montpellier II / CNRS 161, rue Ada 34 392, Montpellier, France
Michel Sala
Laboratory of Computer science, Robotics and Microelectronics of Montpellier
Université Montpellier I / CNRS 161, rue Ada 34 392, Montpellier, France
Keywords: E-learning, Ontology, Metadata, Learning resource representation.
Abstract: E-Learning or online learning refers to the use of computer technologies to design, create, deliver, manage
and support learning for students and help teachers to provide their resources on the internet. And each web
site contains sets of pages and associated indexes. To organize learning resources to facilitate the access to
these resources by teachers or students, many useful queries and computations over such repositories
involve traversal and navigation of the Web graph. In this paper we purposed learning resources
cauterization by applying S-Node graph with respect in ontology or concept structure.
1 INTRODUCTION
E-Learning or online learning refers to the use of
computer technologies to design, create, deliver,
manage and support learning for students and help
teachers to provide their resources on the internet.
For teachers to prepare their courses, searching and
collecting learning resources will be necessary in the
primary step. Material resources come from variety
sources, e.g. website, text book, and in different kind
of format, e.g. text, slide, video, audio, etc.
Imagine in database scope, there are many
courses that concerned with database in many levels,
for example: introduction to database, database
system principles, advanced topics in database
systems, and database system implementation. Each
courses are different in difficulty level but they are
shared the same basic concept. For teachers who
prepare these database courses, it is possible that
they will use or refer to the same materials. A
question is what is the effective way to store and
manage these materials or pedagogical resources for
sharing and reusing?
In our work, we are trying to define the approach
between Concepts, Learning objects and Chapters.
In figure 1, there are three learning objects which are
compositions of chapters. It is possible that a
learning object is used to be taught in one or more
chapters. And each learning object talks about a
concept, these concepts are represented as domain
ontology. We defined a course is a set of chapters
which are arranged in sequence called curriculum.
Normally, when teacher sets up a course, curriculum
of course is a guide line or overview for the course.
In our system, curriculum is builded by teacher or
lectures of that course and links between each
chapter indicate sequence or prerequisite of course.
Figure 1: Compositions of course.
As we aim that in this research, we try to define
relation and representation among three
compositions; Learning objects, Curriculum and
Ontology. In the next section, we describe about the
each compositions and their relations.
532
Suksomboon P., Hérin D. and Sala M. (2007).
PEDAGOGICAL RESOURCES REPRESENTATION IN RESPECT IN ONTOLOGY AND COURSE SECTION.
In Proceedings of the Third International Conference on Web Information Systems and Technologies - Society, e-Business and e-Government /
e-Learning, pages 532-535
DOI: 10.5220/0001282605320535
Copyright
c
SciTePress
2 LEARNING OBJECTS
2.1 Definitions
The first definition is stated by the Learning
Technology Standards Committee at the consortium
IEEE (IEEE, 2002) defined that learning objects are
defined here as any entity, digital or non-digital,
which can be used, re-used or referenced during
technology supported learning. In (Ogbuji, 2006)
described [A Learning Object is] any entity, digital
or non-digital, which can be used, re-used or
referenced during technology supported learning.
In this work, we define learning object is a
smallest unit of learning material that can share or
re-use between each course. For example, course A
has learning material named slide1. And in course B,
there is relation that concerning with some part of
slide1, so the author wants to use only some
concerning part in slide1 in course B. In this
example, we will separate slide1 into two parts;
slide1-1 and slide 1-2 (suppose that slide 1-2 is a
part that concern with course B). Thus, we will have
two learning object; slide1-1 and slide 1-2 (not only
slide1) as shown in figure 2.
Figure 2: Courses and learning objects.
2.2 Metadata for Learning Resources
Metadata is data about data that helps us to achieve
better search results (Brase & Nejdl, 2003). The
educational metadata provide descriptions and
additional information about learning resources (e.g.
multimedia contents, electronic books, software
application, etc.). This information can be used not
only for characterizing the resources but also for
searching, cataloguing and improvement (Santos et
al., 2003). One of the most common metadata
schemes on the Web today is the “Dublin Core
Schema” (DC) by DCMI, The Dublin Core
Metadata Initiative Each Dublin Core element is
defined using a set of 15 attributes from the
ISO/IEC11179 standard for the description of data
elements. The “Learning Objects Metadata
Standard” (LOM) (Learning Technology Standards
Committee of the IEEE, 2002) by the Learning
Technology Standards Committee (LTSC) of the
IEEE was therefore established as an extension of
Dublin Core. Each learning object can now
described using a set of more than 70 attributes
divided into 9 categories. Learning Objects are any
digital resource that can be reused to support
learning (Kolovski et al., 2004).
Learning resources or pedagogical resources can
be described in many aspects. For example, the
“Learning Objects Metadata Standard” (LOM)
(Learning Technology Standards Committee of the
IEEE 2002) by the Learning Technology Standards
Committee (LTSC) of the IEEE describes learning
resource by metadata. In (Bich-Lien et al., 2004)
uses OWL to describe metadata of learning
resources and mentioned some definition from IEEE
LOM to preserve the semantic given by it.
3 CURRICULUM
A course consists of curriculum or outline which is
the overview instruction of course that is organized
by teacher, lecturer or author of the course. This
curriculum or outline is course structure that
normally is separated into small unit called chapters
or section, and each unit can be separated into sub-
unit called sub-chapter or sub-section (see figure 3).
Figure 3: Curriculum or course structure.
Course materials or learning materials can be
possible come from many resources, e.g. textbook,
journal, paper, website, etc. And various formats,
e.g. text, video, slide, audio, etc. In curriculum, units
and sub-units can be ordered and grouped by context
or difficulty of material under consideration of the
author of course.
course B
course A
LO1
slide 1
slide1-1
s
lide1-2
textboo
k
p
a
p
e
r
website
etc ..
Curriculum
Chapter 1 …..
1.1
1.2
Chapter 2 …..
2.1
2.1.1
…….
teache
r
j
ournal
LO2
PEDAGOGICAL RESOURCES REPRESENTATION IN RESPECT IN ONTOLOGY AND COURSE SECTION
533
4 REPRESENTATION OF
COMPOSITIONS OF COURSE
In section 1, we introduced the problems of course
representation with three compositions; Learning
objects, Curriculum and Ontology that we described
in section 2, 3 and 4. Between learning objects and
ontology, we represent this relation with learning
resource management with ontology model and S-
node algorithm to represent relation between
learning objects and curriculum of course (see figure
4).
Figure 4: Three compositions of course.
4.1 Learning Resource Management
with Ontology Model
In our work, ontology is concept that describes the
central pieces of knowledge, the main pieces of
information being taught in a course. Subclass of
concepts are fact, definition, and different kinds of
laws and process (Merceron et al., 2004).
In figure 5 (Bastide et al., 2004) defined the
management of the knowledge is made on three
levels which are interconnected. The first level:
learning objects. It is the lowest level of the data
model. It concerns the storage of the learning objects
without metadata.
The second level: metadata. This level contains
the descriptions of the learning objects. The
metadata generally follow a schema which is defined
by standards such as IEEE LOM or SCORM.
The third level: ontologies. This level contains
the representation of the concepts, the sub-concepts
and the links. This part allows one to organize and to
manage components contained in the previous two
levels. The instances of the ontology model contain
the metadata (Level 2) which are used to describe
the learning objects (Level 1). The learning objects
(Level 1) are described by metadata (Level 2) and
regrouped by ontologies (Level 3).
The main relations which arise in ontologies of
learning objects are the following ones: First, the
relation Be_a_part_of(x,y,i) means that x is a part of
y. Thus, it is necessary to know the resource x if we
want to study the resource y. The value i represents
the validity index of the relation (i.e. Reliable
indication of the relation). In fact, it is a weight. This
value has the same signification in the three
following relations.
Second, the relation Be_explained_by(x,y,i) means
that the resource x can be explained by the resource
y.
Third, the relation Be_required(x,y,i) means that
the resource x needs the resource y as pre-required.
And the last one, the relation Be_suggested(x,y,i)
means that it is better to know the resource y before
making the learning of the resource x. If you are
interested in the resource x you can use it
independently of the resource y. You do not have to
know both resources.
Figure 5: Example of an ontology model for mathematics.
The references supplied by the authors must be
used to create semantic links between two resources.
If a link doesn’t exist between these two resources, a
relation of type "Be_suggested" will be created.
4.2 S-node Graph
S-node representation is a representation for Web
graphs proposed by (Raghavan & Garcia Molina,
2003). It provides two key advantages: First S-Node
representations are highly space-efficient. Such
significant compression allows large Web graphs to
be completely loaded into reasonable amounts of
main memory, speeding up complex graph
computations and mining tasks that require
global/bulk access. Second, the top level graph
serves the role of an index, allowing the relevant
lower-level graphs to be quickly located. S-node
representations reduce query execution time.
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4.2.1 Structure of an S-Node Representation
S-Node representation divides nodes of graphs into
classed, each class of nodes is called supernode.
Two-level S-Node representation of the Web graph
as shown in Figure 6.
Supernode graph contains n vertices (call
supernodes), one for each element of the partition.
Supernodes are linked to each other using directed
edges (called superedges). Superedges are created
based on the following rule: There is a directed
superedge E
i,j
from N
i
to N
j
if there is at least one
page in N
i
that points to some page in N
j
.
Each partition is associated with an intranode
graph. IntraNode
i
represents all the interconnection
between the pages that belong to N
i
.
Figure 6: S-Node representation of a Web graph.
4.2.2 Apply S-node Graph with Learning
Objects Indexing and Categorizing
To represent relation between learning objects and
curriculum of a course by using S-node algorithm,
we define each learning object as a node in lower
level while chapters are super nodes in top level. In
figure 7, for clustering learning resources, we follow
S-node algorithm. This approach will be done
automatically and dynamically by the system.
Figure 7: Learning objects represented with S-node graph.
5 CONCLUSIONS
Our work is addressed the problem of efficiently
way to share learning materials or pedagogical
resources and how to representation courses with
several types of resources. We apply S-node graph,
which provides highly space-efficient and reduce
query execution time, and ontology model. This
work is in progress and we plan to extend the model
and hope to realize some experiments.
REFERENCES
Bastide, G., Pompidor, P., Hérin, D., and M. Sala. (2004).
Integration of an Ontology Manager to Organize the
Sharing of Learning Objects in a Peer-to-Peer
Network: Proceeding of SW-EL'04 Workshop on
Applications of Semantic Web Technologies for E-
Learning in Conjunction with ISWC'04, International
Semantic Web Conference, Hiroshima, Japan, 11-16.
Bich-Lien, D., Yolaine B., and Nacera, B., (2004). “Using
OWL to Describe Pedagogical Resources”.
ICALT’04: Proceeding of the IEEE International
Conference on Advance Learning Technology.
Joensuu, Finland.
Brase, J., and Nejdl, W. (2003). “Ontologies and Metadata
for eLearning” in Handbook on Ontologies, Springer-
Verlag.
IEEE LOM (2002). Institute of Electrical and Electronics
Engineers, Inc., Draft Standard for Learning Object
Metadata., New York, USA, 2002.
Kolovski, V., Jordanov, S., and Galletly, J. (2004). An
Electronic Learning Assistant: Proceeding of
International Conference on Computer Systems and
Technologies CompSysTech’2004, Rousse, Bulgaria,
June, 17-18.
Merceron, A., Oliveira, C., Scholl, M., and Ullrich, C.
(2004). “Mining for Content Re-Use and Exchange –
Solutions and Problems”.
Ogbuji, U., 2006. Thinking XML: Learning Objects
Metadata.http://www.128.ibm.com/developerworks/x
ml/library/x-think21.html
Raghavan. S., and Garcia Molina, H. (2003).
“Representing web graphs”, ICDE'03: Proceeding of
the 19th International Conference on Data
Engineering. 405-416.
Santos, J., Anido, L., and Llamas, M. (2003). On the
Application of the semantic Web Concepts to
Adaptive E-learning: Proceeding of the 3rd IEEE
International Conference on Advance of Learning
Technologies (ICALT’03), July 09-11, 480-489.
Top level
graph
Supernode
Supernode
Superedge
S
u
p
e
r
e
d
g
e
Lower
level
directed
graphs
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