LEARNING TECHNOLOGY SYSTEM ARCHITECTURE BASED

ON AGENTS AND SEMANTIC WEB

Alejandro Canales and Rubén Peredo

Computer Science Research Center of National Polytechnic Institute, Col. Nueva Industrial Vallejo

Del. Gustavo A, Madero, D.F. 07738, Mexico City, Mexico

Keywords: Web-Based Education, Agent-Oriented Programming, IRLCOO.

Abstract: The paper presents a new Learning Technology System Architecture that is implemented using agents and

semantic Web. The architecture is divided into client and server parts to facilitate adaptivity in various

configurations such as online, offline and mobile scenarios. The implementation of this approach to

architecture is discusses in SiDeC (authoring tool) and Evaluation System, which are researching,

evaluating and developing an integrated system for Web-Based Education, with powerful adaptivity for the

management, authoring, delivery and monitoring of such material.

1 INTRODUCTION

The use of Web-Based Education (WBE) as a mode

of study is due to the increase in the number of

students and limited learning content resources

available to meet a wide range of personal needs,

backgrounds, expectations, skills, levels, etc. In this

way, the purpose of the delivery process is very

important, because it means to produce learning

content and to present it to the learner in multimedia

form. Nowadays, there are approaches over this

process that focus on new paradigms to produce and

deliver quality content for online learning

experiences, such as a special type of labeled

materials called Intelligent Reusable Learning

Components Object Oriented (IRLCOO), developed

by Peredo et al (2005).

IRLCOO are part of a new Learning Technology

System Architecture (LTSA) based on IEEE 1484

specification (IEEE, 2001) and open standards such

as XML (XML, 2006) as a bar coding system and to

make sure that the learning content is interoperable,

the Global IMS Learning Consortium (IMS, 2005),

Advanced Distributed Learning (ADL), and

SCORM (ADL, 2006). This paper is organized as

follows: in Section 2, LTSA and IRLCOO are

described; in Section 3 y 4, the authoring system

called SiDeC and the Evaluation System are

presented; finally, the conclusions are discussed.

2 LEARNING TECHNOLOGY

SYSTEM ARCHITECTURE

Our LTSA is based on layer 3 of IEEE 1484

specification. This architecture is presented in figure

1, and consists in four processes manage by agents:

Learner Entity, Evaluation, Coach, and Delivery

process; two stores: Learner Records and Learning

Resources; and fourteen information workflows.

First, the Coach process has been divided in two

subprocesses: Coach and Virtual Coach. The reason

is because we considered that this process has to

adapt to the learners’ individual needs in a quick

way during the learning process. For this, some

decisions over sequence, activities, examples, etc.,

can be made manually for the coach but in others

cases these decisions can be made automatically for

the virtual coach.

Figure 1: Learning Agents System.

Briefly, the overall operation has the following

form: (1) the learning styles, strategies, methods,

etc., are negotiated among the learner and other

stakeholders and are communicated as learning

127

Canales A. and Peredo R. (2008).

LEARNING TECHNOLOGY SYSTEM ARCHITECTURE BASED ON AGENTS AND SEMANTIC WEB.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - SAIC, pages 127-132

DOI: 10.5220/0001698301270132

 SciTePress

preferences; (2, new proposal) the learner

information (behavior inside the course, e.g.,

trajectory, times, nomadicity, etc.) is stored in the

Learner Records; (3) the learner is observed and

evaluated in the context of multimedia interactions;

(4) the Evaluation produces assessments and/or

learner information; (5) the learner information

(keyboard clicks, mouse clicks, voice response,

choices, written responses, etc., all over learner’s

evaluation) is stored in the learner history data-base;

(6) the Coach reviews the learner's assessment and

learner information, such as preferences, past

performance history, and, possibly, future learning

objectives; (7, new proposal) the Virtual Coach

reviews the learner’s behavior and learner

information, and automatic and smartly he makes

dynamic modifications on the course sequence

(personalized to learner’s needs) based on the

learning process design; (8) the Coach/Virtual

Coach searches the learning resources, via query and

catalog info, for appropriate learning content; (9) the

Coach/Virtual Coach extracts the locators (e.g.,

URLs) from the available catalog info and passes the

locators to the delivery process, e.g., a lesson plan or

pointers to content; and (10) the Delivery process

extracts the learning content and the learner

information from the Learning Resources and the

Learner Records respectively, based on locators, and

transforms the learning content to an interactive and

adaptive multimedia presentation to the learner.

2.1 IRLCOO Platform

IRLCOO were developed with Flash. Flash is an

integrator of media and have a powerful

programming language denominated ActionScript

3.0 (Adobe, 2007). This language is completely

Object Oriented and enables the design of learning

components that allows multimedia content of side

client. At Run-Time, the components load media

objects and offer a programmable and adaptive

environment to the student's necessities. Flash

already has Smart Clips for the learning elements

denominated Learning Interactions. The aim is to

generate a multimedia library of IRLCOO for WBE

systems with the purpose to separate the content

from the control. Thus, the components use different

levels of code inside the Flash Player. With this

structure, it is possible to generate specialized

components which are small, reusable, and suitable

to integrate them inside a bigger component at Run-

Time by Delivery process. The liberation of

ActionScript version 3.0 inside Adobe Flash©

allows the implementation of the Object Oriented

paradigm. With these facilities IRLCOO are tailored

to the learners’ needs. In addition, this IRLCOO

development platform owns certain communication

functionalities inside the Application Programming

Interface with LMS, Multi-Agent System (MAS),

and different frameworks, as AJAX (Crane, 2006),

Hibernate (Peak, 2006), Struts (Holmes, 2004), etc.,

and dynamic load of assets in Run-Time.

IRLCOO are meta-labeled with the purpose of

complete a similar function as the product bar codes,

which are used to identify the products and to

determine certain characteristics specify of

themselves. This contrast is made with the meta-

labeled Resource Description Framework (RDF-

XML) (RDF, 2004), which allows enabling certain

grade inferences on the materials by means of the

Semantic Web Platform.

2.2 Communication between IRLCOO

and Web Services

The Web Service (WS) standards enable a set of

basic interactions required in a Service Oriented

Architecture (SOA). WS allow access to

functionality via the Web using a set of open

standards that make the interaction independent of

implementation aspects such as the operating system

platform and the programming language used.

ActionScript 3.0 adds the component

WebServiceConnector to connect to WS from the

IRLCOO. The WebServiceConnector component

enables the access to remote methods offered by a

LMS through SOAP protocol. This gives to a WS

the ability to accept parameters and return a result to

the script, in other words, it is possible to access and

join data between public or own WS and the

IRLCOO. It is possible to reduce the programming

time, since a simple instance of the

WebServiceConnector component is used to make

multiple calls to the same functionality within the

LMS. The components discover and invoke WS

using SOAP and UDDI, via middleware and a

JUDDI server (JUDDI, 2005). Placing a Run-Time

layer between a WS client and server dramatically

increases the options for writing smarter, more

dynamic clients. Reducing the needs for hard-coded

dependencies within WS clients (see figure 2). It is

only necessary to use different instances for each

one of the different functionalities. WS can be

unloaded using the component and deployed within

an IRLCOO.

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Figure 2: Web service architecture with IRLCOO.

2.3 Mapping Object/Relational

Hibernate is a powerful, high performance

object/relational persistence and query service.

Hibernate lets develop persistent classes following

object-oriented idiom - including association,

inheritance, polymorphism, composition, and

collections (Hibernate, 2005).

According with figure 1, the “Learner Info

(new)” flow is implemented with Hibernate.

Creating the InfrastructureException class for

Hibernate Errors and Exceptions, this class is used to

manipulate exceptions in the Hibernate code. Later

creating the HibernateUtil class is possible to initiate

Hibernate and get access to a session of the

persistence and transaction operations.

The StudentDAO class encapsulates all

functionalities necessary to persist student’s registers

data in the database using Hibernate. Thus, the

action class will be responsible by get information

from student, pass to persist for the DAO class and

return success or error to the user. Mixing Struts and

Hibernate and creating the StudentAction class, this

class is an adapter between the contents of an

incoming HTTP request and the corresponding

business logic that should be executed by this

process request. The controller (RequestProcessor)

will select an appropriate Action for each request,

create an instance, and call the execute method. The

database configuration in Hibernate is made trough

of the hibernate.cfg.xml file. The mapping between

Student Class and database is carried out in the

Student.hbm.xml file.

3 SiDeC

In order to facilitate the development of learning

content, it was built an authoring system called

SiDeC (Sistema de Desarrollo de eCursos - eCourses

Development System). SiDeC is a system based on

LTSA to facilitate the authoring content to the tutors

who are not willing for handling multimedia

applications. In addition, the Structure and Package

of content multimedia is achieved by the use of

IRLCOO, as the lowest level of content granularity.

SiDeC is used to construct Web-based

courseware from the stored IRLCOO (Learning

Resources), besides enhancing the courseware with

various authoring tools. Developers choose one of

the SiDeC lesson templates and specify the desired

components to be used in each item. At this moment,

the SiDeC lesson templates are based on the

cognitive theory of Conceptual Maps (CM), but in

the future we will consider others theories such as:

Based-Problems Learning (BPL), the cases method,

etc.

SiDeC has a metadata tool that supports the

generation of IRLCOO to provide online courses.

This courseware estimates learners’ metrics with the

purpose to tailor their learning experiences.

Furthermore, the IRLCOO offer a friendly interface

and flexible functionality. These deliverables are

compliance with the specifications of the IRLCOO

and with learning items of SCORM 1.2 Models

(Content Aggregation, Sequencing and Navigation,

and Run Time Environment) (ADL, 2006). Metadata

represent the specific description of the component

and its contents, such as: title, description,

keywords, learning objectives, item type, and rights

of use. The metadata tool provides templates for

entering metadata and storing each component in the

SiDeC or another IMS/IEEE standard repository.

In figure 3, the SiDeC implements the CM as a

navigation map or instructional and learning strategy

allowing to the learner to interact with content

objects along the learning experiences. These

experiences follow an instructional-teaching

strategy. These kinds of strategies carry out

modifications of the learning content structure. Such

modifications are done by the designer of the

learning experience with the objective of provide

significant learning and to teach the learners how to

think (Díaz-Barriga, 2002). The learning content can

be interpreted in a Learning Content Tree.

Figure 3: Learning content generated for the SiDeC.

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3.1 Communication between IRLCOO

and LMS

Our communication model uses an asynchronous

mode in Run-Time Environment (RTE) and joins to

LMS communication API of ADL (ADL, 2006),

AJAX (Asynchronous JavaScript And XML)

(Crane, 2006) and Struts Framework (Holmes, 2004)

for its implementation. The LMS communication

API of ADL consists of a collection of standard

methods to let the Client to communicate with the

LMS. The browser-based communication model is

depicted in figure 4.

Figure 4: Communication model between IRLCOO, LMS

and AJAX and Struts Framework.

According to figure 4, the communication model

starts: (1) when an IRLCOO generates an event. (2)

Form the browser interface is made a JavaScript call

to the function FileName_DoFSCommand

(command,args), which handles all the FSCommand

messages from IRLCOO, LMS communication API,

and AJAX and Struts methods.

The communication with the LMS starts when:

(i) the standard methods call to the Communication

Adapter (written in JavaScript). (ii) The

communication adapter implements the bidirectional

communication ADL´s API between the Client and

the LMS. (iii) The LMS realizes the query-response

handling and the business logic. The purpose of

establishing a common data model is to make sure

that a defined set of information about content can

be tracked by different LMS environments. If, for

example, it is determined that tracking a student’s

score is a general requirement, then it is necessary to

establish a common way for content to report scores

and for LMS environments to process such

information (for interoperability and reuse of such

content). If every chunk of content used its own

unique scoring representation learning management

systems would not know how to receive, store or

process such information. These definitions are

derived from the IEEE Standard for Computer-

Managed Instruction document (P1484.11), which

originated within the Aviation Industry CBT

Committee (AICC) (IEEE, 2005).

The communication with AJAX and Struts

Framework begins when AJAX-Struts method is

called. (3) An instance of the XMLHttpRequest

object is created. Using the open() method, the call

is set up, the URL is set along with the desired

HTTP method, typically GET or POST. The request

is actually triggered via a call to the send() method.

(4) A request is made to the server, this might be

a call to a servlet or any server side technique. (5)

The Controller is a servlet which coordinates all

applications activities, such as: reception of user

data, data validations, and control flow. The

Controller is configured for a XML file. The

Controller calls to perform method of Action, it

passes to this method the data values and the Action

reviews the characteristic data that correspond to the

Model. The business objects (JavaBeans) realize the

business logic, (5) usually a database access by

Hibernate. The Action sends the response to the

Controller. The Controller reroutes and generates the

interface for the results to the View (JSPs). The

View makes the query to the Business objects based

on the correspondent interface. (6) The request is

returned to the browser. The ContentType is set to

text/xml, the XMLHttpRequest object can process

results only of the text/ html type. In more complex

instances, the response might be quite involved and

include JavaScript, DOM manipulation, or other

technologies. The XMLHttpRequest object calls the

function callback() when the processing returns.

This function checks the readyState property on the

XMLHttpRequest object and then looks at the status

code returned from the server. (7) Provided

everything is as expected, the callback() function

sends HTML code and it does something interesting

on the client, i.e. advanced dynamic sequence.

This communication model provides new wide

perspectives for the WBE systems development,

because it provides the capabilities of communica-

tion, interaction, interoperability, security, and

reusability, between different technologies. For

example, the LMS communication API allows us to

make standard database queries of learners’

information such as personal information, scores,

assigned courses, trajectory, etc. While the

communication with AJAX and Struts Framework

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provides the capability of modify the learner’s

trajectory according to variables from the learner

records in RTE (advanced dynamic sequence),

components management (IRLCOO) – remember

that these components are built and programming

with XML – then, this model provides the way to

write, load, change and erase XML files in the

Server side.

4 EVALUATION SYSTEM

The Evaluation System for WBE is designed under

the same philosophy used for the SiDeC. The

functionality of the Evaluation System lays on the

analysis of the learner’s profile, which is built during

the teaching-learning experiences. The profile is

based on metrics that elicited from the learner’s

behavior at Run-Time. These measures are stored

into the learner records that compose the profile. The

generation of new sequences of courses is in

function of the results obtained, besides the account

of the adaptation level.

The Evaluation System combines IRLCOOs,

additional meta-labels, and a Java Agent platform.

Also, some technologies of the Artificial Intelligence

field are considered in order to recreate a Semantic

Web environment. Semantic Web aims for assisting

human users to achieve their online activities.

In resume, the components and operation of the

SiDeC and Evaluation System are outlined in figure

5. Basically the Evaluation System is fulfilled

through two phases. The first phase is supported by

the LMS, and is devoted to present the course and its

structure. All the actions are registered and the

presentation of the contents is realized with

IRLCOO content. The evaluations are done by

evaluating IRLCOO and in some cases by simulators

based on IRLCOO. These processes are deployed by

the Framework of Servlets, JSPs and JavaBeans.

The second phase analyzes the learner's records

carried out by the Server based on JADE MAS. This

agent platform owns seven agents: Snooper, Buffer,

Learner, Evaluation, Delivering, Coach, and Info.

The fundamental idea is to automate the learner's

analysis through the Coach/Virtual Coach, and to

give partial results that can be useful for the learner's

final instruction. These agents are implemented as

Java-Beans programs, which are embedded in the

applications running both at the client and server

sides. The Snooper Agent works as a trigger by

means of the INFORM performative, which

activates the MAS server’s part. This agent is

deployed into a Java Server Page that uses a

JavaBean. During the lesson or once evaluation is

finished, the graphical user interface activates the

Snooper Agent and sends it the behavior or

evaluation metrics (using Agents Communications

Language or FIPA ACL (FIPA, 2001) to be

analyzed at the server-side of the MAS. The Snooper

Agent activates the system, whereas the Buffer

Agent manages the connection and all the messages

from the client. Both tasks are buffered and send

them to the Coach Agent. Then the Coach Agent

requests to the learner records for the preferences

learner, trajectory, previous learner monitoring

information, etc. The Coach Agents analyzes this

information to determine if the learner needs help. If

this situation is true, the Coach Agent requests to the

learning resources the needful learning content

(URLs) and it sends the learning contents (URLs) to

the Delivery Agent. The Delivery Agent sends the

learning content to the Learner and Evaluation

Agents for its presentation. These agents employ the

dynamic sequencing to change the course or

assessment sequence. The sequencing is defined for

the instructional strategy based on CM and it

employs the SCORM Sequencing/Navigation. Once

the necessary information is received (sequence,

kind of IRLCOO and localization, etc.), this is

represented as a string, which is constructed

dynamically by the rule-based inference engine

known as JENA (JENA, 2006) and JOSEKI server

(JOSEKI, 2006), to generate dynamic feedback.

Figure 5: Semantic Web Platform for WBE.

4.1 Semantic Web Platform

The overall architecture of Semantic Web Platform,

which includes three basic aspects (see figure 5):

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131

1. The query engine receives queries and

answers them by checking the content of the

databases that were filled by info agent and

inference engine.

2. The database manager is the backbone of

the entire systems. It receives facts from the info

agent, exchanges facts as input and output with the

inferen-ce engine, and provides facts to the query

engine.

3. The inference engine use facts and

ontologies to derive additional factual knowledge

that is only provided implicated. It frees knowledge

providers from the burden of specifying each fact

explicitly.

Again, ontologies are the overall structuring

principle. The info agent uses them to extracts facts,

the inference engine to infer facts, the database

manager to structure the database, and query engine

to provide help in formulating queries.

JENA was selected as the inference engine. It is

a Java framework for building Semantic Web

applications. It provides a programmatic environ-

ment for RDF, RDFS, OWL, SPARQL and includes

a rule-based inference engine (JENA, 2006).

While JOSEKI was selected as Web API and

server. It is an HTTP and SOAP engine supports the

SPARQL Protocol and the SPARQL RDF Query

language. SPARQL is developed by the W3C RDF

Data Access Working Group (JOSEKI, 2006).

5 CONCLUSIONS

LTSA, IRLCOO and Semantic Web Platform allow

developing authoring and evaluation systems to

create adaptive and intelligent WBE. Our approach

focus on: reusability, accessibility, durability, and,

interoperability of the learning contents.

The communication model composes for the

LMS communication API, AJAX and Struts

Framework, IRLCOO, WS, Semantic Web, and

JUDDI. It provides new development capabilities for

WBE systems, because their integrant technologies

are complementary. SiDeC and the Evaluation

System were developed under this model to help in

the automation and reduce of the complexity of the

learning content process.

The incorporation of Web Semantic Platforms

helps us to create intelligent and adaptive systems

(bidirectional communication), according to the

users’ needs.

ACKNOWLEDGEMENTS

Authors of this paper would like to thank the

Instituto Politécnico Nacional (IPN), Centro de

Investigación en Computación (CIC) for the partial

support for this project within the project IPN

20071166.

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