A TOOL FOR MANAGING DOMAIN KNOWLEDGE IN

INTELLIGENT TUTORING SYSTEMS

Panayiotis Kyriakou, Ioannis Hatzilygeroudis and John Garofalakis

Department of Computer Engineering & Informatics

University of Patras, Patras, Greece

Keywords: Information system, knowledge system, meta-data management, intelligent tutoring system.

Abstract: Domain knowledge (DK) is a basic part of an intelligent tutoring system (ITS). DK usually includes

information about the concepts the ITS is dealing with and the teaching material itself, which can be

considered as a set of learning objects (LOs). LOs are described by a data set called learning object

metadata. Concepts are usually organized in a network, called a concept network or map. Each concept is

associated with a number of LOs. In this paper, we present a tool for managing both types of information in

DM: creating and editing (a) a concept network and (b) learning object metadata. Additionally, the tool can

produce corresponding XML descriptions for each learning object metadata. Existing tools do not offer all

the above capabilities.

1 INTRODUCTION

Recent developments in computer-based educational

systems gave rise to a new generation of them

encompassing intelligence in order to increase their

effectiveness, called intelligent educational systems.

Intelligent Tutoring Systems (ITSs) constitute a

popular type of intelligent educational systems. ITSs

take into account the user’s knowledge level and

skills and adapt presentation of the teaching material

to the needs and abilities of individual users. This is

achieved by using Artificial Intelligence techniques

to represent pedagogical decisions as well as domain

knowledge and information regarding each student.

ITSs were usually developed as stand-alone systems.

However, the emergence of the WWW gave rise to

Web-based ITSs (Brusilovski, 1999;

Hatzilygeroudis, 2004).

The structure of an ITS is illustrated in Figure 1.

An ITS consists of three main modules: (a) the

domain knowledge, which contains the teaching

content and meta-information about the subject to be

taught, (b) the user model, which records

information concerning the user, and (c) the

pedagogical model, which encompasses knowledge

regarding various pedagogical decisions.

In the domain knowledge, the teaching material

must be structured in such a way that can be easily

recognized and used by the pedagogical unit, in

order to adapt teaching to user’s needs. A quite

helpful way is to distinguish between the teaching

material itself and its meta-information, typically

called its metadata. The teaching material itself

usually consists of learning objects (LOs), which are

autonomous, self-contained digital entities (e.g. web

pages) used to support learning (Wikipedia, 2008).

To be able to manage domain knowledge in an ITS,

we need a tool that will be able to manage LOs

metadata and real teaching material too. However,

apart from those, domain knowledge also contains

information about the concepts the system is

concerned with. Although there are a number of

tools dealing with management of metadata for LOs,

most of them are not suitable for dealing with

concepts. On the other hand, although there are tools

dealing with concepts, they are not able to relate

them with LOs.

In this paper, we present a tool that is able to deal

with both LOs and their metadata description as well

as with concepts and their relations. Also, it helps

tutors in preparing lessons. Description of LOs

metadata is based on the IEEE LOM standard (IEEE

LTSC, 2002; Holzinger et all, 2001).

Kyriakou P., Hatzilygeroudis I. and Garofalakis J. (2008).

A TOOL FOR MANAGING DOMAIN KNOWLEDGE IN INTELLIGENT TUTORING SYSTEMS.

In Proceedings of the Third International Conference on Software and Data Technologies - ISDM/ABF, pages 5-11

DOI: 10.5220/0001872100050011

 SciTePress

Figure 1: The Basic Structure of an Intelligent Tutoring

System.

The paper is structured as follows. Section 2

presents the structure of domain knowledge in an

ITS and its requirements of managing it. Section 3

deals with the functional characteristics of the

introduced tool, while Section 4 presents some

design and implementation issues. Section 5 presents

related work and shows the inadequacies of existing

tools. Finally, Section 5 concludes the paper.

2 DOMAIN KNOWLEDGE

The motivation for creating such a tool was the need

for managing the domain knowledge of a certain

ITS. To be able to manage its domain knowledge, it

has been structured as displayed in Figure 2. So, it

consists of three components: knowledge concepts,

course units and meta-description.

The knowledge concepts are elementary pieces

of information of the specific domain. Every

concept has a number of general characteristics such

as the name, the difficulty level, the detail level, the

prerequisite knowledge, etc. Moreover, a concept

has relations with the other concepts which mainly

show the prerequisite concepts that contain the

perquisite knowledge for that concept. For example,

in teaching logic in an Artificial Intelligence course,

‘logic syntax’ and ‘logic semantics’ could be two

concepts. ‘logic syntax’ could have as prerequisites

the concepts ‘constant’, ‘variable’, function’ etc.

However, there may be other types of relations, like

“generalizes”, “specializes”, “part-of” etc. For

example, ‘left-hand expression’ may be “part-of”

‘implies expression’. The concepts and their links

form a network (see Figure 3), which is a semantic

network that represents the pedagogical structure of

the teaching subject.

The teaching material consists of two parts: (a)

the course units and (b) the meta-description. The

course units mainly are in the form of web pages and

are equivalent to LOs. Each course unit may contain

a variety of data types (e.g. text, images, animations

etc).

Course units are used in composing lessons.

Each course unit is related to a concept and through

the concept network, that has been created, the

system chooses the next course unit (web page) to be

presented to the user. A course unit can be of a

theory, an example or an exercise type. Examples

help the student to understand in a better way the

theory. Exercises are based on the examples and are

used to evaluate the knowledge level of the user.

This information is used to update the model of the

user.

Figure 2: The Structure of Domain Knowledge.

Figure 3: A Concept Network.

The knowledge domain also contains meta-

descriptions of the course units and their main

attributes. Such attributes are mainly its difficulty

level, its pedagogical type (theory, example,

exercise), its representation type (text, image,

animation), its detail level, etc. Those meta-

Domain

Knowledge

Pedagogical

Unit

User

Mdli

User Interface

Knowledge

Concepts

…

Meta-

description

Course Units

ICSOFT 2008 - International Conference on Software and Data Technologies

descriptions of the course units are based on the

IEEE LOM standard schema (IEEE LTSC, 2002).

3 A TOOL FOR MANAGING

DOMAIN KNOWLEDGE

We created a tool for managing the domain

knowledge of an ITS based on the above. So, using

the tool one can create the concept network of a

domain, using a variety of relation links, which

him/herself can define. Also, can create, store, view

and edit the metadata for the LOs (i.e. course units)

in XML format.

3.1 Concept Map

The tool provides facilities for easily creating a map

(network) of the concepts involved in the domain.

Figure 5: New Concept Creation.

To create a new concept, one can click on the tool

workspace and choose “Add concept”. Then the

interactive form of Fig. 5 appears where he/she can

define the name of the new concept.

To create a relation between two concepts, one

should click on the

icon, lying at the right-hand

side of a concept (Fig. 7), and then on the related

concept. The interactive form of Figure 6 appears

where he/she can choose a relation type from a list

of existing ones or create a new type of relation.

Figure 7 shows two concepts connected with a

“requires” relation. At each concept one can see, at

the first glance, the name of the concept and its

relations with other concepts. Also, when the mouse

is over a concept one can see more icons that

perform certain functions. The

icon calls the

renaming function; the

icon calls the function

that deletes a relation; and the

icon deletes the

selected concept.

Figure 6: Form for adding a relation.

The user can insert concepts at any position of the

concept map, drag the concepts across the map in

order to organize them, create new relations and

connect them. In Figure 8, a concept network, where

concepts are connected with various types of

relations, is presented.

Figure 7: Concept Map Creation.

For the convenience of the users, there is a search

bar, which we can give in any keyword related to

concepts or to the metadata of the learning objects.

The result is to center the concept map to the

selected concept and highlight it.

3.2 Managing Learning Objects

The learning objects (course units) attached (i.e.

related) to each concept can be displayed by double

clicking on the corresponding concept (see Fig. 9).

We can add/create a learning object, by clicking on

the “Add Learning object” link or delete any of the

existing ones (not shown in Fig. 9).

Having displayed the learning objects, we can do

two other things:

(a) Display and edit its metadata, by clicking on

the “Edit Metadata” link of a learning object

(see Figure 9). Its metadata appears in a

structured way (see Figure 10) so that can be

easily read and edited (i.e. modified/deleted),

using the corresponding link in each metadata

category. The structure of metadata follows

the IEEE LOM standard. On this set of

metadata there is the ‘Relation’ category,

which however is not the same as the

‘relation’ between concepts. It specifies

relationships between the learning objects

(course units).

A TOOL FOR MANAGING DOMAIN KNOWLEDGE IN INTELLIGENT TUTORING SYSTEMS

Figure 8: A Concept Map (Network).

Figure 9: Displaying Learning Objects.

(b) Create an XML file representing the object’s

metadata. The information that we have

specified in the concept metadata is stored in

our database as well as in XML files for

future use. These XML files can be accessed

by other elements of any other system that

has access to our file system, which through

that can decide on the selection of the

concepts that will use in a tutoring session.

Figure 10: Displaying and Editing Metadata of a Learning

Object.

Therefore, we have included access to these

files through links of our system. To see the

XML file of a LO we should just click on the

“Create XML file” link of the LO (Fig. 9).

The XML file is then dynamically created

(Fig. 11).

ICSOFT 2008 - International Conference on Software and Data Technologies

Figure 11: The XML file of a Learning Object.

4 SYSTEM DESIGN AND

IMPLEMEMNTATION

The heart of the system is the relational database,

which is designed to store all vital information

concerning the concept description and connections,

the LOs metadata as well user related data. The

database is organized in 17 tables, apart from the

one related to ‘learning object’ (see Fig. 12), 11 for

metadata, 5 for the concept map and 1 for user

identification.

WAMP5 version 1.6.1, containing Apache 1.3.,

PHP5 and MySQL4, was used for the

implementation of the system. MySQL is used for

the database, whereas PHP for the rest of the system,

like e.g. the creation of XML files. For the concept

map drawing, javascript was used from the Open-

JACOB Draw2D library and the scipt.aculo.us

library. The queries to MySQL are made through

AJAX technology that connects the index.html file

with the php files.

5 RELATED WORK

We distinguish two categories of tools to compare

with ours. There are a number of tools, which are

used for the management of metadata in learning

objects. In spite of their many features, none of them

was satisfactory to be used with the ITS.

LOM Editor (LOM-Editor, 2001) developed by

Darmstadt University of Technology -Germany, and

written in Java, is a standalone desktop application

which includes superior abilities for editing

metadata, such as: Tabular presentation of metadata

categories; vocabulary management; multiple-

language values management; metadata template

generation to avoid the necessity to repeatedly enter

the same data in multiple fields. Some of the

drawbacks of this metadata authoring tool include:

No help or documentation; omission of specific

details of LOM Model (e.g. multiple-language

values support for metadata elements); some

standards like vCard and ISO 8601 DateTime

standard are not supported in the representation of

the metadata elements; storage of the metadata

record is done in a database and there is no export

option for the XML document.

ALOHA II (ALOHA II, 2004) is a Java-based

tool that is used for indexing, aggregating, sharing,

multi-purposing, and re-purposing learning objects.

It is created to meet the needs of indexers, educators

and learners and includes versatile and powerful

indexing tools and flexible searching of multiple

educational object repositories. The software is

based on the educational standards of IMS and

SCORM. ALOMA II is not web-based and not

based on the IEEE LOM standard.

Curriculum Online Tagging Tool (Curriculum

Online Tagging Tool, 2008) which is designed to

make the process of creating metadata and

outputting as easy and intuitive as possible. A

version of this tool is being developed to support UK

LOM Core and aspects of CanCore. It enables

creation and storage of details about the learning

resources. It also allows adding those details to the

Curriculum Online portal, so that teachers can find

out about the learning resources. Once the details

about a resource are added to the portal, the tagging

tool can be used to update them at any time, or even

remove them completely. This tool cannot create a

network of resources, but just an unstructured

repository of them.

Explor@-II (EXPLORA-II, 2008) is a software

environment for the delivery of courses or distance

learning events on the Internet. It allows creating a

virtual training centre that delivers a set of courses

on the Internet according to a variety of models and

using a LO repository facilitating information

access, production, follow-up and coaching of

learners as well as training management. It is fully

compatible with the IEEE LOM, Cancore and

Normetic. Explor@-II is more an e-learning

environment, not web-based, rather than a domain

knowledge management tool.

A TOOL FOR MANAGING DOMAIN KNOWLEDGE IN INTELLIGENT TUTORING SYSTEMS

Figure 12: Database Relations Diagram.

The second category concerns tools that mainly

deal with creating concepts and concept maps.

Inspiration (Inspiration, 2008) uses a web as a basic

graph structure, and repositions the initially entered

concept in the middle of the screen. This tool does

not enforce any particular graph structure, and the

representation does not require linking phrases. The

software allows the user to switch from graph to

outline view and back. SMART Ideas (SMART

Ideas, 2008) allows users to create multi-level

Concept Maps to organize ideas, to link Concept

Maps to files and Web sites, to switch between

diagram and outline views, and to publish Concept

Maps on the Web. LifeMap (LifeMap, 2008) is

designed for free educational use. Group packages

with support are available. LifeMap provides the

capability to make Vee diagrams as described in

(Novak and Gowin, 1984). IHMC CmapTools

software kit (Cañas et al, 2003a) is to enable users to

collaborate during Concept Map construction and to

easily share and publish the resulting knowledge

models. The software is based on a client-server

architecture (Cañas et al, 2003b) that allows users to

share and browse Concept Maps stored in

CmapServers distributed throughout a network that

covers the whole world. Luckie Concept Connector

is a software suite currently in development at

Michigan State University. This system allows

ICSOFT 2008 - International Conference on Software and Data Technologies

students to build Concept Maps online and to

receive immediate feedback about their maps based

on automatic scoring systems that are derived from

scoring methods detailed in (Novak and Gowin,

1984). The Concept Mapping system is based upon a

pre-defined set of concepts and linking phrases. The

system is currently being used for online homework

assignments.

6 CONCLUSIONS

In this paper, we present a tool that is suitable for

managing the domain knowledge in an ITS. More

specifically, the tool allows for creating, viewing,

editing and deleting knowledge concepts, which are

organized in a network (map). Concepts are

connected between each other by a variety of

relations (e.g. “requires”, “isa” etc), which can be

user defined. Also, it allows associating concepts

with corresponding learning objects, i.e. real

teaching material. In addition, the user has the

capability of adding to or modifying metadata

(which complies with IEEE LOM data model) of

each learning object associated with a concept and

create XML files which he/she can view and edit in

the process.

All the above are not offered by existing tools, as

reported in Section 5. We’ve used the tool for the

creation of the domain knowledge of an ITS

teaching “Intelligent Tutoring Systems” (see Figure

8) and another one teaching “Aspects of Artificial

Intelligence”. It has been really very helpful.

REFERENCES

ALOHA II, 2004. http://aloha.netera.ca/about.php

Brusilovski P., 1999. Adaptive and Intelligent

Technologies for Web-based Education. In C.

Rollinger and C. Peylo (Eds), Kustliche Intelligenz,

Special Issue on Intelligent Systems and Teleteaching,

4, 19-25.

Cañas, A. J., Hill, G., Carff, R. and Suri, N., 2003a.

CmapTools: A knowledge modeling and sharing

toolkit, Technical Report IHMC CmapTools 93-01,

Institute for Human and Machine Cognition.

Cañas, A. J., G. Hill, A. Granados, J. D. Pérez and C.

Pérez, 2003b. The network architecture of CmapTools,

Technical Report IHMC CmapTools 93-02, Institute

for Human and Machine Cognition.

Curriculum Online Tagging Tool, 2008. http://www.

curriculumonline.gov.uk/SupplierCentre/

taggingtool.htm. Accessed: March, 2008.

EXPLORA-II, 2008. http://explora2.licef.teluq.uquebec.

ca/ demo/. Accessed: March, 2008.

Hatzilygeroudis I., Guest Editor. Special Issue on AI

Techniques in Web-Based Educational Systems.

International Journal on AI Tools (IJAIT), 13(2),

2004.

IEEE LTSC, 2002. Draft Standard for Learning Objects

Metadata. http://ltsc.ieee.org/wg12/files/

LOM_1484_12_1_v1_Final_Draft.pdf .

Holzinger, A., Kleinberger, Th. and Müller, P., 2001.

Multimedia Learning Systems based on IEEE

Learning Object Metadata (LOM), Proceedings of

ED-MEDIA 2001 World Conference on Educational

Multimedia, Hypermedia & Telecommunications,

Tampere, Finland, June 25-30.

Inspiration, 2008: http://www.inspiration.com/home. cfm.

Accessed: March, 2008.

LifeMap, 2008. http://www.robertabrams.net/

conceptmap/lifemaphome.html. Accessed: March,

2008.

LOM-Editor, 2001. Darmstadt University of Technology:

http://www.multibook.de/lom/en/ index.html.

Novak, J. D. & Gowin, D. B., 1984. Learning how to

learn. New York: Cambridge University Press.

SMART Ideas, 2008. http://www.smarttech.

com/products/smartideas/index.asp. Accessed: March,

2008.

Wikipedia, 2008. http://en.wikipedia.org/wiki/

Learning_object_metadata

A TOOL FOR MANAGING DOMAIN KNOWLEDGE IN INTELLIGENT TUTORING SYSTEMS