Pedagogical Model based on Semantic Web Rule Language

Camila Bezerra da Silva

Center for Exact Sciences and TechnologyFederal University of Recncavo of Bahia - UFRB

Cruz das Almas - Bahiacamila.bezerra.br@gmail.com

Abstract—Intelligent Tutoring System(ITS) aims to providepersonalized instruction. Its main module, pedagogical model,is responsible for choosing which strategy to apply to aspecific student, as well as determining which learning topicsto provide. However, there are several strategies and teachingtechniques, and each student has his peculiarities. This paperproposes the use of Semantic Web Rule Language(SWRL) toassist ITS in the making decision on which learning strategyto apply to a specific student in a given time. SWRL isbased on Horn-like rules expressed in terms of Ontology WebLanguage(OWL) concepts.

Keywords-Learning; Ontology; SWRL; Tutoring;

I. INTRODUCTION

Concerning to Intelligent Tutoring Systems(ITS), the pe-

dagogical model is responsible for choosing the strategy and

tactics that will be applied to a particular student, based on

the interaction that this student will have with the system.

The main goal of ITS is to provide a personalized teaching to

the student. Therefore, it is necessary to know the student’s

needs, and through the student profile to infer what strategy

to apply in a certain time.

In last decades, pedagogy researchers have developed

many teaching strategies, aiming to make learning activities

more efficient. Some of these strategies have been persistent

over the years, as the strategies had been based on the

behaviorist and constructivist theories. Computer systems

applied to education have used these theories. However,

some questions are still open: Which strategy to apply to

a student in a given time and when to change the strategy?

Which topics on the subject to be studied display? In what

order?

In order to answer these questions, it has been used

Artificial Intelligence techniques such as ontologies, rules-

based systems and intelligent agents, and with these tech-

niques such good results have been achieved( [1], [2]).

Ontologies are used to represent knowledge of a domain. In

the case of tutoring systems, ontologies attend to represent

the knowledge to be taught and the learner profile.

Following this line, this paper proposes the use of on-

tologies and Semantic Web Rule Language(SWRL) to build

the pedagogical model. SWRL emerged from deficiency of

ontologies that can not represent Horn-like rules. SWRL

rules are written in the antecedent-consequent form. The

antecedent and consequent consist of a conjunction of one

or more atoms expressed in terms of OWL concepts.

Thus, by using a rule engine like Jess [3], it is possible

to reason about such rules and answers the questions posed

previously, i.e, to know what strategy to apply to a student

at any given time, according to his interaction.

This paper is organized as follows: section II describes

some of main concepts about Intelligent Tutoring Systems

and Pedagogical Theories; section III presents Ontologies

and the Semantic Web Rule Language(SWRL); section IV

describes the pedagogical model based on SWRL; section V

is devoted to related work, and section VI finish this paper

with some conclusions.

II. INTELLIGENT TUTORING SYSTEMS

The Intelligent Tutoring Systems(ITS) appeared in the

90’s with the main objective of providing tools that meet

the individual needs of each student.

ITS consists of computer systems that support learning

and try to adapt to the needs of the particular student.

These systems incorporate Artificial Intelligence techniques

for knowledge representation, reasoning and user interaction.

Moreover, ITS offers an individualized learning process

according to student’s needs. To achieve this goal, ITS

knowledge base is updated and adapts its teaching strategies

according to student interaction.

According to [4], [5] and [6] the key features of an ITS

are:

• The domain knowledge is represented explicitly and

unambiguously, being possible to make inferences on

such knowledge.

• The student acquires knowledge that allows the ITS to

manage and adapt learning.

• The sequence of teaching is not predetermined, it can

adapt.

• It can generate appropriate problems, tips and it adapts

to student’s needs.

These systems have basic architecture consists of four

components: student model, pedagogical model, domain

model and interface, as shown in Figure 1.

2012 12th International Conference on Computational Science and Its Applications

978-0-7695-4710-7/12 $26.00 © 2012 IEEE

DOI 10.1109/ICCSA.2012.31

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Figure 1. architecture

In the student model is represented the knowledge, behav-

ior and cognitive characteristics of the student. The domain

model contains the domain knowledge of the subjects to

be taught or learned. The pedagogical model contains the

teaching strategies and tactics that are used according to

student interaction with the system. Finally, the interface

mediates the interaction between tutor and a student.

This paper is centered in the pedagogical model that is

responsible for the organization and selection of teaching

strategies and tactics to be used in interaction with the

student, thus providing a personalized teaching.

In the next section, it was described the main pedagogical

theories.

A. Pedagogical Theories

There is still no pedagogical theory for distance learning.

However, it has been adopted traditional pedagogical the-

ories, which are already consolidated, and they have been

applied in the teaching process in general. The trend is the

use flexible of different pedagogical theories, to meet the

diversity of resources and students.

The most popular theories are Behaviorist, Constructivist-

Interactionism and Constructivist Social-Interactionism, and

in each of these theories, their representatives stand out:

Skinner, Piaget and Vygotsky, respectively.

The Behavourism [7] had its origin in the 50’s, when pro-

fessor Skinner of Harvard has proposed a teaching machine.

According to this theory, students are taught in way that they

are induced to engage in new forms of behavior and specific

ways. The material to be taught is transmitted in a linear

and sequential way. By allowing students to interact with

a learning content, it is not stimulated learner autonomy,

which is faced with a sequential structure.

With the crisis of the behaviorist paradigm, came into play

in the mid 50’s: the cognitive psychology, which the main

feature is the construction of knowledge through information

processing. From this, emerged a new approach to teaching:

the Constructivism.

The Constructivist-Interactionism theory suggests that the

learner understands the world through his perceptions, by

building meant for this world [7].

Piaget, its main defender, believed that learning occurs

in stages that are directly linked to the mental development

of each student. This theory are centered on individual de-

veloping, each learner should construct his own knowledge,

without take account the social-history context.

In this context, the goal of the teacher would be to

encourage individual discovery, and no more than determine

the speed and way of construction of knowledge of the

student.

Parallel to the development of Constructivist-

Interactionism, computer systems have emerged with

Artificial Intelligence, and appeared several studies that

relate Education, Web and Artificial Intelligence. Such area

that came up with this joint is called e-learning.

Finally, the Social-Interactionism approach proposed by

Vygotsky had as the main objective the interaction between

an individual with others [7]. According to this theory,

human intelligence is constituted by cultural tools, such

as language that are the legacy of past generations and,

therefore, can only be understood from a socio-historical

perspective of human cognition.

Vygotsky proposed the concept of Zone of Proximal

Development, where he said that what an individual ac-

complishs with the help of another individual, as a tutor

or a friend, or even resources such as books, videos and

computer, it also represents an intellectual ability of the

individual. Unlike the Piaget’s approach which consider as

intellectual ability, only what each individual was able to

build by himself without interactions with others.

The role of the teacher in this context is to promote

social harmony by stimulating the exchange of information

in search of building shared knowledge. Furthermore, the

computation is now seen as a means of communication

between tutors and learners.

Besides these theories, there are several tactics of teaching

that can be used in the learning process. Each tactic is related

to one or more theories. So for every theory, there are several

tactics that can be employed in learning.

The problem is to know which tactics to apply to a

student, by considering that each student has own skills and

deficiencies.

To solve this problem, this paper proposes to use Ontolo-

gies and Semantic Web Rule Language, which it is explained

in the next section.

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III. ONTOLOGY AND SEMANTIC WEB RULE LANGUAGE

An ontology can be described as an explicit and formal

specification of a domain which someone has an interest

in representing. Ontologies are based on Description Logic

that consist of concepts and relationships between these

concepts that are organized in a hierarchical structure [8].

Moreover, it is worth highlighting that it is possible to

represent disjunction and combination of classes, property

restrictions and characteristics of such properties.

The set of concepts and their relationships of domain

are called knowledge base. Through this knowledge base, a

computer system can interpret it and derive new knowledge,

by returning useful information as well as inferring implicit

information to the user.

Usually, in a certain area of knowledge, different experts

have different understanding of the concepts involved, which

leads to problems will be in communication, and ambiguity.

Ontologies provide standardization to e-Learning systems,

by reaching a shared vocabulary consensus in their unders-

tanding of a domain.

OWL is the language recommended by The World Wide

Web Consortium (W3C) [9] to the specification of ontologies

for the emerging Semantic Web technologies [10]. OWL is

a family of knowledge representation with different levels

of expressivity: OWL-Lite, OWL-DL and OWL-Full. This

paper focuses on OWL-DL which is based on Description

Logics(DL) [11]. Besides, OWL-DL has a well-defined

syntax, a formal semantic, convenience of expression and

support for reasoning efficiently.

An OWL-DL ontology, as a DL based ontology, does

not support rules, which limits expressivity. There are cases

where it is need to express some definitions and this is only

possible with such rules.

Consider an ontology for the domain of a family. In

the Description Logic used to specify ontologies, it is not

possible to have a rule that expresses “the brother of the

father of a person is his uncle”. To accomplish this, it came

up the Semantic Web Rule Language(SWRL) that is also

recommended by W3C.

SWRL [12] is a rule description language which

combines RuleML and OWL. It allows users to create rules

in the Horn clauses format [13] that can be expressed in

terms of OWL classes. In addition, it can reason about

OWL individuals. Thus, with rule like below, it can be

solved the problem described in the example above:

father(?x, ?y) ∧ brother(?x, ?z) ∧ man(?z) →uncle(?z, ?y)

What means that a person ?y that has a father ?x and

this father ?x has a brother ?z, that implies the person ?z is

his uncle.

SWRL rules are in the form antecedent → consequent,

which is interpreted so that if the antecedent is true, then

the consequent must also true. Moreover, both the antecedent

and the consequent parts are conjunctions of atoms, accor-

ding the Horn clauses [13].

In this context, ontologies are used in e-Learning to

represent the learning domain, and SWRL are used to

express rules that can not be expressed in OWL language.

In the next section, it will be described the pedagogical

model based on semantic web rule language.

IV. PEDAGOGICAL MODEL BASED ON SEMANTIC WEB

RULE LANGUAGE

As we saw, there are some consolidated learning theories.

Each one with its own characteristics, and provide the

student a different way of learning. However, each student

has a different way of assimilating the content to be studied,

some learning more by studying alone, some in groups, some

reading, some watching a presentation, etc...

The questions that arise are: What strategy to apply to

each student? How can offer personalized learning? What

sequence of learning topics offer?

These are issues that this paper proposes solve, by using

Semantic Web technologies.

The first step is to identify characteristics of the student,

cognitive aspects and about the domain to be studied. This

process results in the student model, which is represented

by ontologies. The student model is updated according to

student interaction with the system.

For this, a preliminary analysis is performed, which it is

given a questionnaire to the student with questions about the

learning domain and cognitive aspects.

Both the questionnaire follows the IMS as the ontology

which represent the student model [14]. The IMS specifi-

cation defines several profiles of information that must be

obtained from the student in order to take a better knowledge

of the capabilities, intentions and actions of this.

In addition, it is being used questions of the Learning

Styles Questionnaire( [15]) that defines several of questions

to infer the type of student.

The original questionnaire is in portuguese language, and

adopted has questions such as:

What content topics the student has studied?

What the student has not studied?

Do you prefer study alone or in a group?

Do you prefer study with a linear content module or a non-

linear content like hypertexts?

Do you learn better texts with pictures and diagrams inside

or not?

Based on the answers of the questions, an initial profile

of the student is created, and selected which strategy and

tactics to use, initially. Therefore, there is a relationship

between strategies and tactics that are represented in the

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Figure 2. Pedagogical Model

ontology, moreover questions and their alternatives are also

represented in this ontology.

The strategies considered in this work are Be-

haviorist, Constructivist-Interactionism and Constructivist

Interactionism-Social, that were explained in the previous

section II-A. There are several teaching tactics that are

consistent with these strategies, and so, these tactics are

grouped by strategy.

The ontology was built using the Protege [16], which is

an ontology editor. Part of the ontology that exhibits the

relationship between the strategies and tactics, is showing

in Figure 2.

The questions are presented to the student in a non-

linear sequence, the initial questions related to student

cognition, aims to find out which strategy fits more with

the student. For example, by considering the question below:

I prefer studya).a linear learning module, in other words, i like thatlearning content be given to me in a sequential andpredefined structure.b).a free content, choosing the topics by self.

If the student chose the first option, it is possible to

infer that he fits better with behavioral strategy, which

believes that students should follow a sequential structure,

linear and predefined. Otherwise, if he chooses the second

option, certainly the most appropriate strategy would be

constructivist.

It was noted that such decisions about which strategy

and/or tactics would be best interpreted using SWRL, which

as already stated, is a language for building rules like:

premises → conclusion, following the definition of the Horn

clauses.

So, the question above can be expressed in SWRL:

Figure 3. SWRL Rules

Person(?x) ∧ Question(I prefer to study) ∧Choice(a linear learning module) →applyStrategy(Behaviorist, ?x)

Which means that IF a person X AND the question is “I

prefer to study” AND the choice was “a linear learning

module” THEN apply the Behaviourist strategy for person

X.

Inside the Protege, there is the “SWRL Tab” that is

an extension to the Protege-OWL plugin that permits the

creation and execution of SWRL rules. Some rules of

pedagogical model are shown in Figure 3.

To run this rules, and so infer what strategy and tactics to

apply, it was used the SWRLJessTab that is a plugin to the

SWRLTab in Protege that supports the execution of SWRL

rules using the Jess rule engine [3].

After create the OWL concepts and necessary SWRL rules

in the Jess, it can be perform inference. When execute the

rules, new Jess facts are inserted into the fact base, and those

facts can be used in further inference.

A. Preliminary Results

This proposed application has been applied to Brazilian

students of course of exact sciences and technology, of the

Federal University of Reconcavo of Bahia.

In the present stage, it was carried out a part of the tests,

which it was analyzed the performance of the application

with 30 students of the mentioned course. The students are

between 17 and 35 years, and most are male.

The pedagogical model was applied to them, and also

the students were asked about the tactics of teaching that

they learned better. Then, it was verified if the tactics

recommended by the model, in fact, matched the tactics

outlined by the student.

In these previous tests, the proposed model achieved an

average of 85% accuracy.

V. RELATED WORK

During the last decades, there are an increasing and active

researches about computer systems to Education. And a lot

of events was held about this theme.

Recently, [17] propose a system and a set of strategies that

provide a way to generate, automatically, multiple choice

questions from SWRL rules. It has been designed and

implemented as a framework that support new question and

answer formation strategies. The system consists of three

layers. The first layer is responsible for parsing rule files and

to convert them into an internal representation. The second

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layer is responsible for generating a textual representation

for each variable. The last layer is the one responsible for

the generation of the appropriate distracting answers. The

system is implemented in such a way that it possible to

addition new strategies.

Since [18], defends the idea of using SWRL rules to

represent teaching strategies and convert them to Jess for

the environment where they can be tested. The conversion

mechanism makes use of existing tools, like SweetRules that

provide a suite of converters among several rule languages

and rule engines.

Despite the importance of these works to area, none

of them, it was taken into account the different teaching

strategies, much less been a survey of tactics that can be

used in conjunction with such strategies. Moreover, it was

not analyzed the characteristics of each student, to resulting

in the student model, and thus by helping to provide a

personalized learning.

VI. CONCLUSION AND FUTURE WORK

As shown in this paper, Web Semantic technologies can

be applied for Education. The main module of Tutoring

Intelligent System is the pedagogical model, which is re-

sponsible for choosing the strategy and tactics that will be

applied to a particular student, according to his interaction

with the system. The most popular learning theories are

Behaviorist, Constructivist-Interactionism and Constructivist

Social-Interactionism.

As we seen, this can be done with Semantic Web Rule

Language(SWRL) that is a language to write rules-like Horn.

To run this rules, and so infer what strategy and tactics to

apply, it was used a rule engine, the Jess, where users can

run SWRL rules interactively to create new OWL concepts

and then insert them into an OWL knowledge base, or just

verify the validity of the rule.

Thereat, it was concluded that Web Semantic technologies

can be applied efficiently to meet the goals of Tutoring In-

telligent Systems, in particular, to build pedagogical model.

Regarding to future work, we intend to improve the form

of student assessment, by building a more robust model

of the student. Thus, the pedagogical model may suggest

actions with more certainty.

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