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

 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

lide1-2

textboo

website

etc ..

Curriculum

Chapter 1 …..

1.1

1.2

Chapter 2 …..

2.1

2.1.1

…….

teache

ournal

LO2

PEDAGOGICAL RESOURCES REPRESENTATION IN RESPECT IN ONTOLOGY AND COURSE SECTION

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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

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

to N

if there is at least one

page in N

that points to some page in N

Each partition is associated with an intranode

graph. IntraNode

represents all the interconnection

between the pages that belong to N

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.

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Top level

graph

Supernode

Superedge

Lower

level

directed

graphs

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