A LEARNER-CENTERED SEMANTIC WEB-BASED

ARCHITECTURE

T. Kerkiri, A. Manitsaris and I. Mavridis

Dept. of Applied Informatics University of Macedonia

156 Egnatia Street, 54006, Thessaloniki, Greece

Keywords: e-Learning, e-Reputation metadata, KAON, Semantic Web.

Abstract: In this paper the architecture of a learner-centered e-Learning system, which aims to qualify and recom-

mend learning material using semantic web technologies, is presented. In the proposed approach, the learner

has a central role in the decision-making process of the distribution of learning material. The main idea re-

lies on gathering appropriate reputation metadata from the learning material consumers. As a result of a

proper material ranking, a better matching to future searches of learners having similar profile is anticipated.

An experimental implementation of the presented architecture provided interesting results that better depict

the applicability and usefulness of the proposals.

1 INTRODUCTION

The Web today is oriented to the semantic technolo-

gies which intend to make data-search meaningful,

more accurate, and detectable by machines. In se-

mantic technologies the well-formed metadata is

mainly used to express semantics and the material-

retrieval is based on it. One of the fields, among

others, that adopt the emerging technologies of the

Semantic Web is the e-Learning area. Generally

speaking, e-Learning systems are mainly attended by

adults (Rogers 1999), so they have to provide per-

sonalized knowledge, dedicated to their abilities,

skills, and knowledge demands (Brusislovski 2003).

Moreover, these systems have to promote co-

operation among their attendants in order to facili-

tate the learning procedure, to overcome problems

that arise from distance, and to elevate the learners’

personalities. Today, in Internet applications, the

elevation of the users’ role and the promotion of

collaboration among them is a major trend. This is

more and more obvious in modern e-Marketplaces

(e.g. Amazon, e-Bay, etc) which yearn for users’

participation and, at the same time, they take advan-

tage of their opinions. In the same idea, the blogs

and the social networking sites are con-

structed(e.g.www.mySpace.com,www.del.ici.ous.co

m, www.hi5.com, etc). Our work is based on the

idea that the role of the attendants is of great impor-

tance in e-Learning systems, too. Their opinions,

expressed as evaluation metadata, can improve the

functionality of the system. According to our ap-

proach, the metadata in e-Learning systems is not

static, as it is in common nowadays. It is not only

defined by the material-provider during the initiali-

zation phase of the system, but it can change radi-

cally while the system is running. Such continuously

provided accurate and countable e-Reputation meta-

data (Dellarocas et al. 2002) and (Kerkiri et al.

2006), is collected and exploited to improve the

documents’ distribution, the learning resources’

quality, and to guide future searches of learners hav-

ing similar profile.

This paper is structured as follows: in section 2,

related work on the semantic web ontological tools

is presented. In section 3 a proposed modular archi-

tecture of a learner-centered system is presented. An

experimental implementation of the proposed system

architecture for an e-Learning environment is pre-

sented and evaluated in section 4. Finally, our con-

clusions and future work are discussed in the last

section.

2 RELATED WORK - ISSUES

Web semantic systems are based on metadata which

describe their material and on ontologies which clas-

sify their content and inference from it. Generally

speaking, such systems: i) provide an editor to create

the ontology, expressed in a standard ontological

563

Kerkiri T., Manitsaris A. and Mavridis I. (2007).

A LEARNER-CENTERED SEMANTIC WEB-BASED ARCHITECTURE.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Society, e-Business and e-Government /

e-Learning, pages 563-566

DOI: 10.5220/0001291205630566

 SciTePress

language (e.g. RDF, OWL), and to manipulate its

instances, ii) support a standardized method (e.g.

RDQL, RQL, RDF-QEL, SeRQL, etc) for searching

into the repositories and for sharing their contents,

iii) are able to adopt newer standards (e.g. OWL).

Some of the current semantic products/tools, like

Protégé (available at http://protege.stanford.edu), are

specialized in creating-editing ontologies. Others,

like Sesame (available at http://www.opendf.org)

emphasize on the repository sharing. SHAME

(available at http://kmr.nada.kth.se/shame/wiki/Ov-

erview/Main) is focused on the creation of special-

ized RDF-schema repositories, and especially on

educational metadata schemas, like LOM (avaliable

at IEEE Learning Technology Standards Committee.

2001, IEEE LOM Working Draft 6.1. http://ltsc.ieee.

org/wg12/index.html), DublinCore, etc. KAON

(Volz et al. 2003), OntoStudio (available at

http://www.ontoprise.de) and Sesame provide a cen-

tral container for their metadata-repository, while the

Edutella educational network (Nejdl et al. 2002)

uses P2P technologies to share its material. More-

over, KAON, Sesame and OntoStudio provide a

specific database schema that simulates the RDF and

OWL framework. Some of them create an integrated

environment; others are focused just in just one ca-

pability (editing/querying) and they do the rest of the

job with plugins. All the above mentioned products

are general purpose tools that both create the ontol-

ogy for a specific application, and manipulate its

instances, as well.

We need a semantic web based system, dedi-

cated to e-Learning area that can provide specialized

services for this field.

3 THE PROPOSED

ARCHITECTURE

A schematic representation of the proposed architec-

ture, in correlation to the three phases of a method-

ology that aims to guide the implementation of a

semantically web based knowledge management

system (Kerkiri et al. 2004), is depicted in figure 1.

The architecture is designed in a modular manner

to undertake the advantages of such approach. The

architecture is based on a central RDF-based server

which collects the metadata of all e-Learning system

participants. The central repository lets the learners

to take advantage of the system’s dynamically in-

coming up-to-date metadata. Each node that wants

to find material using this system is given access on

the RDF-Server. Every annotation which is inserted

on the central repository stands as an advertisement

for newly inserted learning resources. If a participant

wants to contribute as a learning-resource provider

as well, a local material repository must be created.

On each of these material-repositories autonomous

local sharing and security policies can be applied.

According to this view the system stands as a con-

tinuously updated encyclopaedia of the knowledge-

domain defined by the ontology.

Figure 1: The modules of the proposed architecture.

The proposed architecture consists of three main

modules:

1. Knowledge creation module: it creates/edits

the ontology and handles its instances during the

knowledge creation phase. It separates the interfaces

of the learner and the learning resources’ provider

and makes them adaptable to their needs. It also de-

termines the different permissions that providers and

consumers both have upon the documents. It con-

sists of two sub-modules:

1.1. Οntology handling sub-module: it is imple-

mented in three distinct sub-systems:

1.1.1 General-annotation sub-module: it is a

general purpose RDF-editor.

1.1.2 Learning-material handling sub-module: it

provides a unique id (URI) to each learning re-

source, so that the resource can be attached the

proper properties, according to its definition.

1.1.3 Learner-handling module: it creates the

learner’s profile, and defines the access policy and

the authorizations he has on the system.

1.2 Correlation sub-module: it creates correla-

tions among the documents and their URIs. During

the retrieval phase it undertakes the applying of each

node’s policy upon the documents it distributes.

2. Search and retrieval module: it accepts the

learners’ criteria, translates them to suitable RDF-

expressions and looks for appropriate learning re-

sources. This module allows searching even into

sub-concepts of the ontology. It consists of two sub-

modules:

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2.1 Search sub-module: this module i) applies

the suitable verifications regarding the learner’s ca-

pability of searching for learning material and ii)

transforms every RDF-triple (object, non-attribute

property, literal value) to an appropriate searching

clause.

2.2 Retrieval sub-module: it undertakes the con-

trol over the shared learning resources of the node.

Using the URI metadata the actual position of the

learning material is found into the network; then the

learner’s authorizations’ are checked, and, if permit-

ted, the document is revealed to the Learner.

3. e-Reputation handling module: it lets author-

ized learners to evaluate the documents, according:

i) to their context, using standard evaluation vari-

ables and ii) to the metadata they have obtained by

their providers. Three sub-modules implement the e-

Reputation metadata process:

3.1 e-Reputation collection module: it deals with

problems commonly appeared in Reputation-

systems: identity checking and e-Reputation meta-

data providers’ privacy. After the checking, e-

Reputation metadata is collected in cooperation to

the annotation sub-module.

3.2 e-Reputation metadata processing sub-

module: it creates sub-sets of i) the learners accord-

ing to the metadata included in their profile, and ii)

the learning objects, according to both their annota-

tions and their reputations.

3.3 e-Reputation inference sub-module: this

module i) uses the results of the e-Reputation meta-

data processing sub-module in order to propose to

future learners, having similar profile, documents

that could be suitable for them ii) uses the criteria

that the learner has stated during the learning mate-

rial search and combines them to their profile.

4 AN EXPERIMENTAL

IMPLE-MENTATION

To experience the proposed architecture, an applica-

tion has been developed. The actual implementation

of the central knowledge-base repository is based on

the structure of a database system borrowed from

KAON (Motik et al. 2002), which simulates the

RDF. Using the ontology handling sub-module of

the above mentioned application, an ontology was

created, based on LOM standard. The main concepts

of an e-Learning system according to LOM are: the

“LearningResource”, having properties that facilitate

its retrieval, and the “Learner”, having properties

(his profile) that facilitate the system to find the ap-

propriate learning resources for him. The ontology

classifies the material which supports the “Multime-

dia” course for the students of our educational insti-

tute. The instances of this ontology were correlated

to learning material that was distributed in 10 con-

tributed nodes, using the correlation sub-module,

providing 183 different learning resources. The aim

of this implementation was to provide suitable re-

sources to the learners needs, matching the proper-

ties of these two concepts, during the knowledge

retrieval phase.

Apart from these standard LOM entries, two new

concepts were introduced in the ontology:

i) e-Reputation concept: the instances of this

concept intend to evaluate each of the learning re-

sources metadata that has been provided from the

learning resource providers. Properties of this con-

cept may be evaluations for anyone of the criteria

that are used to classify/retrieve the learning re-

sources.

ii) EvaluationCriteria concept: instances of this

concept are criteria like: “Usability”, “Originality”,

“Comprehensitivity”, “Scientificity”, etc, that anno-

tate the content of the learning resource. Techniques

and variables used to evaluate user-centered adapt-

able systems can be found at a study of Gena (2005).

51 users, having different permissions, were

given access to the system to implement the knowl-

edge creation phase. According to their permissions,

the users were able to i) create annotations, ii) pro-

vide resources, and iii) make reputations. For dem-

onstration purposes, 452 different metadata-

searching properties, divided in 11 categories (cate-

gories 1-11, depicted in figure 2), were inserted into

the central data repository. Two more properties (12-

13) were introduced to record the satisfaction of the

system usage and its provided results.

Figure 2: The 15 categories of criteria. Statistics before

and after the 1

rst

cycle of the system operation are de-

picted.

During the search and retrieval phase: i) many

queries were posed and their searching-criteria were

A LEARNER-CENTERED SEMANTIC WEB-BASED ARCHITECTURE

565

recorded, ii) the participants provided their evalua-

tions in a scale from 0 to 1, (evaluation collection

sub-module) using the previous mentioned e-

Reputation-Evaluation criteria. 561 different reputa-

tion metadata have been provided. ii) After experi-

enced the system, the learners provided their own

material in 27 cases. In 88 cases, and for a variety of

these criteria, the average was less than 0,4. In all

these cases the providers revisited their material and

changed their metadata (15,68%) to better match to

the learners’ proposed evaluations.

During the e-Reputation-Feedback exploitation

phase, the system was asked to find resources by

combining the learners’ profile metadata, as well as

the properties of the learning resources and the e-

Reputations other learners had provided. The e-

Reputation processing sub-module capabilities were

used to propose material according to this metadata

combination. According to a study of Kerkiri

(2006), the mediator used to exploit the e-Reputation

metadata was a SQLServer-2005 view that provided

the accumulative evaluation of any identical e-

Reputation criterion. More over, suitable stored pro-

cedures were created to inference from the view’s

contents. During this phase new reputations we re

provided. The average mean of the next-step reputa-

tions augmented, for each of the criteria, by a means

of 0.3 points, according to the initial average (fig.2).

Two more criteria (14-15) were added after the first

evaluation phase, to find out if the learners were

motivated to participate.

As interesting consequences of this implementa-

tion can be considered the following facts: i) the

ordering of the resources was changed, according to

the ranking they gathered, ii) less results were re-

trieved in each query, iii) better matching of the

learning resources to the “suitability” criterion was

recorded.

5 CONCLUSIONS – FUTURE

WORK

In this paper a modular architecture, based on educa-

tional standards and Semantic Web technologies,

which aims to share knowledge over an e-Leaning

network is presented. The knowledge-consumer of a

system that conforms to the proposed architecture

has a central role in the overall functionality. Each

learner can participate to this system according to

his permissions by creating annotations, providing

his own learning resources, or/and providing his

countable e-Reputation metadata. The e-Reputation

metadata is of great significant in this architecture

and it is exploited to make the learning resources’

metadata more accurate, to improve the quality of

the learning resources context, to recommend mate-

rial suitable to each learner’s profile, and to promote

co-operation among learners.

To demonstrate the advantages of the proposals,

an experimental implementation of an e-Learning

system, has been provided. As depicted in the previ-

ous section, the system gradually improves its re-

sults on providing personalized resources, after hav-

ing collected a great amount of e-Reputation meta-

data.

In our future plans is to develop the complete

ranking process of the learning resources and to im-

prove the functionality of the e-Reputation inference

sub-module.

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