Fuzzy Querying based Tool for Building Courses Evaluation Tests

Livia Borjas

, Josué Ramírez

, Rosseline Rodríguez

and Leonid Tineo

Departamento de Informática, IUT Federico Rivero Palacios, Caracas, Venezuela

Departamento de Computación, Universidad Simón Bolívar, Caracas, Venezuela

Keywords: Computer Aided Education, e-Learning, Fuzzy Querying.

Abstract: In this paper, we present a tool intended for helping in exam configuration based on the reutilization of

questions according to user preferences. It is a real life application of fuzzy querying that fulfils an actual

need of academic personal at a high studies institution of Venezuela. This application uses the fuzzy

querying language SQLf, on top of an existing relational DBMS by means a logic layer named SQLfi. Final

users of our application are professors of any area without knowledge of fuzzy sets and databases. We use

criteria for the test preparation that support fuzzy terms. These terms can be adjusted to user preferences.

Graphic user interfaces are provided in order to perform such adjusts as well as exam configuration and any

other fuzzy querying operation. We present here the Database Design, the process test construction and the

management of preferences.

1 INTRODUCTION

Professors throughout their carriers have to measure

the performance of their students basically through

the application of written tests. The design of these

tests might become sometimes a discouraging

process given its repetitive nature. Furthermore,

some professors, due to their work as researchers

and because of the administrative burden, do not

have enough time to dedicate to the preparation of

tests using all the creativity and awareness required.

By virtue thereof, we propose the creation of a

software tool for supporting tests’ preparation

process. In connection therewith, the needs of

professors will be considered the following criteria

for the preparation of a test or question: difficulty

(for example: low, moderate/low, moderate, high

and very high), time to solve it (for example: long,

moderate and short), correction time (for example:

long, moderate and short). The aforementioned

criteria are not accurate; therefore the definition of

fuzzy terms is required. These terms are subjective

since, for each term, each professor will differently

conceive the range of values. Also, these terms do

show the present imprecision at this application, as

well as the feasibility of the fuzzy logic use in its

queries.

Additionally, the preparation of tests is

commonly based on existent tests to benefit from

previous experiences. Consequently, a database

storing this information is required. The use of these

data might require the definition of searches based

on preferences including fuzzy terms.

Another important factor is the consideration of

the grades or results obtained in previous tests (for

example: outstanding, excellent, good, regular and

poor), which allows professors to project the

expected performance of their students in a test.

This might be considered through the generation

of statistics based on non-accurate gradual

requirements defined by professors. It is also

important for professors to have a scenario providing

potential different tests based on established criteria,

allowing for making the decision of choosing which

tests better fit to the needs of the students being

evaluated at a given time.

We present here a fuzzy querying based

application for support professor’s work of

preparing evaluation tests. This application is made

in the frame of an effort of the Venezuelan IUT

Federico Rivero Palacio of providing professors

some computer aided support tools for academic

work.

This paper has the following structure: Section 2

presents Fuzziness in Databases. Section 3 briefly

introduces the Fuzzy Querying Language SQLf that

is used in this development research. Section 4

describes main System Features such as the

Database Design, the functionality of Examination

343

Borjas L., Ramírez J., Rodríguez R. and Tineo L..

Fuzzy Querying based Tool for Building Courses Evaluation Tests.

DOI: 10.5220/0004151003430349

In Proceedings of the 4th International Joint Conference on Computational Intelligence (FCTA-2012), pages 343-349

ISBN: 978-989-8565-33-4

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Tests Construction with fuzzy queries expressing

user desires and the Management of Preferences.

Section 5 is devoted to giving some Concluding

Remarks and Future works.

2 FUZZINESS IN DATABASES

The growing ambitious use of information systems

in the amount of data volume and data recovery

mechanisms has fostered the accelerated

development of technology. In this regard, query

specification mechanisms that resembling natural

language expression and human thought is sought.

Therefore, it would be convenient to provide flexible

query capabilities allowing users to express

requirements involving preferences.

A powerful tool used in the management of

databases for the purpose of making queries in a

more flexible way has been the fuzzy sets (Zadeh,

1965; Cox, 1995; Galindo, 2008; Pivert and Bosc,

2012), which vests traditional databases with a more

intuitive approach closed to the natural language

when retrieving information from data sources. This

type of sets has been applied to several knowledge

areas, such as Control Systems, Decision-making

Systems, and Expert Systems. In (Ma and Yan,

2010), it is stated that the theory of fuzzy sets has

been widely applied to extend several models and

management systems database, which has resulted in

many contributions. In this regard, different research

projects have been conducted worldwide focused on

including fuzziness in databases (Bosc and Pivert,

1995a; Kacpryzyk and Zadrozny, 1995; Goncalves

and Tineo, 2008; Galindo, 2008; Yager, 2010; Ma

and Ya, 2010; Pivert and Bosc, 2012). Nevertheless,

there are few known developed applications that

enjoy the benefits of fuzzy databases (Galindo et al,

2006; Goncalves and Tineo, 2008; Fernandez et al,

2008; Delgado et al, 2008).

We spoke of imprecision in database when the

information is incomplete or uncertain. Thus, an

approximate value or a term describing it is stored

instead of an exact value. In general, we could be

talking about data inaccuracies or imprecision in

queries, i.e., imprecise terms may be stored as data

values or could be used in the query criteria. Thus

there are mainly two different approaches that

address the vagueness of the databases. Those

focused on the data (Galindo, 2008) and those

focused on queries (Pivert and Bosc, 2012).

The results produced by an application system,

based on conventional DBMS, strictly adjust to the

compliance with the search criteria. But, they are not

considering other results that approximate in certain

degree to the response; these could be presented to

user as alternate results. Search criteria in the

systems based on classic DBMS are accurate, while

Fuzzy DBMS allow for defining more flexible

criteria that better adjust to human thought. Classical

databases use all the results obtained, while Fuzzy

Querying Systems only use those results that meet

the minimum level of satisfaction defined by users.

Classical queries limit their results since are based

on the use of Boolean Logic, where a proposition

only accepts two values: true or false.

Systems based on fuzzy databases are closer to

human through. The use of fuzzy logic better adapts

to real world and it even is able to understand and

deal with linguistic terms or expressions in natural

language, such as "it is really easy", "he is not too

long", “he is too short”, etc.

3 FUZZY QUERYING

LANGUAGE

Different fuzzy query languages have been proposed

(Bosc and Pivert, 1995a; Galindo, 2005; Kacpryzyk

and Zadrozny, 1995; Nakajima et al, 1995). On of

the most remarkable efforts is SQLf (Bosc and

Pivert, 1995a). It is an extended SQL with the

application of the Fuzzy Set Theory aimed at

expressing flexible queries on Relational Databases.

This query language has been updated with different

versions of SQL (Goncalves et al, 2009). SQLf

allows the use of different fuzzy elements in

constructions of SQL:2003 where a condition is

involved.

For the purpose of defining the preferences of

each user, SQLf counts on a series of sentences

belonging to the DDL (Data Definition Language)

for fuzzy components. In this (SQLf - DDL) terms

such as predicates, modifiers, quantifiers and

comparators are involved, which have a meaning

that is specific to a group of users or individuals.

Fuzzy predicates are the atomic components of

fuzzy logic. These correspond to the class of terms

known as "linguistic labels". In SQLf, fuzzy

predicates are defined through the following

sentence:

CREATE FUZZY PREDICATE <name> ON

Where <domain> is a range of scale characters

defined by the user, <fuzzyset> is a specification

of the fuzzy set, which membership function be

defined by

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 a trapezium with the syntax:

(<value1>,<value2>,<value3>,<value4>)

using the “infinite” special value, the

semantics of which is infinite;

 extension with the syntax: {<d

>/<v

>, ... ,

>/<v

>} where <d

> is a degree and <v

is a value.

Example: Predicate of Figure 1 is defined in

SQLf by means the sentence:

CREATE FUZZY PREDICATE excellent

ON 0..20 AS (15,18,20,20)



1/2

0 ... 15 ... 18 ... 20

Figure 1: Membership function for the fuzzy predicate

excellent, defined as (15,18,20,20).

An example for a predicate defined by extension

is the following:

CREATE FUZZY PREDICATE favorite

ON topicName AS {

1.0/design,

0.3/querying,

0.5/implementation,

0.9/programming

}

As ever, SQLf has different querying structures

for data manipulation. The main query structure in

SQLf is the multirelational basic block. The main

difference with respect to SQL is that the condition

of the clause WHERE is a fuzzy condition. SQLf also

includes the selection of the best answers according

to their satisfaction degree that is named the

calibration. Another difference is that the result of

the query is not a bag but a fuzzy bag. The syntax of

basic block is:

SELECT [DISTINCT] <attributes>

FROM <relations>

WHERE <fuzzy condition>

[WITH CALIBRATION k |  | k, ]

For obtaining, the results with excellent score,

the fuzzy predicate excellent defined earlier by the

user, is used in the following statement:

SELECT * FROM RESULT

WHERE score = excellent;

A significant difference between fuzzy querying

and traditional Boolean querying is as follows: In a

Boolean query, we establish crisp filtering criteria.

Selected rows may be after ordered according

sorting criteria or attributes. Selection and ordering

are made on independent crisp criteria. On the other

hand, in a fuzzy query, we establish flexible

selection criteria. Such criteria are based on fuzzy

logic involving linguistic terms. They express user

preferences. Fuzzy criteria are combined giving a

global satisfaction degree for each retrieved tuple.

This degree is a measure of user preference

fulfilment. Results should be automatically sorted in

decreasing ordered of satisfaction degrees. It would

be helpful in decision-making process.

In SQLf, it is possible to express the calibration

of the solution, i.e. the selection of the best answers,

in two senses. First, the qualitative sense where we

indicate a minimum level of tolerance . Only

tuples with satisfaction degree greater or equal to 

are retrieved. Second, the quantitative senses

indicating a maximum number of desired answers k.

Best k tuples according their satisfaction degree are

retrieved. It is also possible to combine both senses

in the querying statement. This is the effect of the

clause WITH CALIBRATION.

4 SYSTEM FEATURES

We have developed an Automated System for the

Preparation and Configuration of Tests named

SAECE for its words in Spanish language. Remark

that this is an application for a Venezuelan

educational institute, the IUT Federico Rivero

Palacios, therefore interfaces and linguistic terms are

also given in Spanish that is the official language in

Venezuela. Through the SAECE, professors might

express their requests by using terms that are more

similar to the commonly used natural language.

Thus, we avoid inflexible queries that do not include

their preferences. Additionally, the system facilitates

the preparation and configuration of evaluations

and/or tests based on existent tests, as well as the

definition of search preferences and generation of

statistics based on queries with non-accurate gradual

requirements defined by users. SQLfi (Goncalves

and Tineo, 2008), a server of SQLf, was used for the

implementation of SAECE. SQLfi works as a layer

that connects to the Oracle database manager and

applies the Derivation Principle (Bosc and Pivert,

1995b) to evaluate a fuzzy query over the result of a

classical query avoiding extra rows access in

querying processing and keeping low the added cost

of fuzzy querying features.

With this system, professors may create new

tests based on existent tests; defining therefore

parameters such as difficulty, duration, level of

difficulty, time of response, time of correction.

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4.1 Database Design

SAECE has a database that allows for retrieving

tests created that might be configured in all their

parameters. The idea is to have a test questions bank

to be reused in different evaluations. The

configuration of new evaluations would be made

with this system aid. A simplified version of the ER

diagram for this database is in Figure 2.

The PROFESSOR entity contains the user

information as login, password, name, gender, and

address. The QUESTION entity covers information

over tests' questions or specific topic questions, as

type, description, time answer, time of correction,

difficulty, and references. The EVAL TEST entity

contains information such as the evaluation name,

date of elaboration, rating scale, percentage and

description. The TOPIC SUBJET entity contains the

different topics that a test has or the topics that a

course covers. This entity keeps the topic name,

time, references and description. The RESULT

entity saves the test results to generate statistics that

user wish. This information includes date,

description and score. The COURSE entity contains

relevant information of courses such as name and

description. There is a relationship (evaluates) for

indicate the topics including in a test and the

different tests that evaluate a topic. Also, there is a

relationship (about) to indicate the questions that

deal a topic. There is a relationship to show the

questions that compound a test. Also, it is possible

that a question can to belong different tests. This

relationship contains an attribute to indicate

weighting of questions. There are relationships to

indicate the evaluation tests that belongs to a

professor, the results associates to an evaluation test,

the professors that teaches the courses, the topics

associates to the courses and the topics subordinate

to another topics.

4.2 Examination Tests Construction

Tests might be partially recovered (certain

questions) or totally recovered through search

criteria based on fuzzy logics. A professor might

also add questions to an existent test. SAECE allows

for conducting different statistics such as:

outstanding, excellent, good, bad and poor grades

for the results obtained from a test application.

Difficulty: This is modelled in a rank of values

between 0 and 5, being 5 the highest difficulty and 0

the lowest. This search will be made based on the

criteria that the user has defined for each difficulty.

For example the system has like predetermined

configuration that the low difficulty is from 0 to 0.5,

but this can be modified and the professor can place

the values to him that consider appropriate for that

difficulty.

Time of Correction: Possibly when the

professors were created the new evaluations, to each

question of the evaluation, they assign a time of

correction. As well as the criterion of the difficulty,

the system also has a configuration predetermined

for this criterion that the professor can modify.

Time to solve it: For this criterion it is applied

just like the Time of Correction.

Precision of the search: The number of questions

that the system throws will depend on the precision

of the search with which it is desired to collect the

data. While upper it is the degree of the precision,

there are possibilities that less results are obtained.

The 100% of precision is the highest exigency. One

is due to consider that the search can be done by the

three criteria or by those which the user wishes. For

example, a search can be made of: number of

questions: 3; difficulty: medium; time of correction:

Figure 2: SAECE database Entity-Relationship diagram.

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346

medium; time to solve it: medium; and precision:

20%.

SAECE brings a single interface for user choice

of criteria (Figure 3). From this selection, SAECE

builds the SQLf statement for being processed in

SQLfi fuzzy querying system. In this case, the fuzzy

query in SQLf should be the following one:

SELECT q.questionId, q.wording,

q.type, q.difficulty,

q.correctionTime,

q.answerTime

FROM QUESTION q, TOPIC t,

COURSE m,

ABOUT tq

WHERE m.login = ’josueramirez’ AND

m.courseId = ’programacion’ AND

t.topicId = 1

AND tq.topicId = t.topicId

AND tq.questionId = q.questionId AND

q.difficulty = high

AND q.correctionTime = medium

AND q.answerTime = medium

WITH CALIBRATION 3, 0.2

This fuzzy query in SAECE should return a result as

that shown in Figure 4.

4.3 Management of Preferences

SAECE has a set of linguistic terms for the

expression of user preferences. These terms are used

to describe imprecise conditions on questions

attributes such as: difficulty, time of correction, time

to solve it, and score. The set contains different

terms for different attributes. Moreover, each user

may give terms own interpretation and change it

arbitrary at anytime.

The specification of these terms semantics is

based on fuzzy sets. Nevertheless, SAECE provides

a graphic user interface for establish preferences.

This interface generates corresponding DDL

statements in SQLf.

For example, for the score (nota) attribute,

SAECE has the linguistic terms deficient

(deficiente), regular, good (buena), excellent

(excelente) and outstanding (sobresaliente). These

terms are interpreted as fuzzy predicates defined by

trapezium. Figure 5 shows the interface for these

terms specification. This interface is rather intuitive

to be handled by a final user not familiarized with

fuzzy sets and fuzzy databases.

These linguistic terms of Figure 5 would be

created with the SQLf statements:

CREATE FUZZY PREDICATE deficient

ON 0..20 AS (0,0,7,10);

CREATE FUZZY PREDICATE regular

ON 0..20 AS (7,10,12,20);

CREATE FUZZY PREDICATE good

ON 0..20 AS (12,15,16,20);

CREATE FUZZY PREDICATE excellent

ON 0..20 AS (16,17,18,20);

CREATE FUZZY PREDICATE outstanding

ON 0..20 AS (18,19,20,20);

It is also possible to change the scale range of

test grades. If so, the built-in values are

automatically adjusted to fit the scale.

5 CONCLUDING REMARKS

In the presented tool, user is able to define and use

qualitative and quantitative selection criteria based

on linguistic terms expressing user preferences. If a

similar system were built with a classic querying

language, it would impose more rigid selection

criteria, limiting thus the expression of user

Figure 3: End user fuzzy querying interface. Here the user can settle down the criteria search for the questions that wish to

assign to the examination according to criteria difficulty (dificultad), time of correction (tiempo de corrección), and time to

solve it (respuesta).

FuzzyQueryingbasedToolforBuildingCoursesEvaluationTests

347

Figure 4: Here are the results obtained according to the selected criteria. As it is possible to be observed the thrown

questions, satisfaction degrees are associated to each one of the subjects selected according to the selected criteria of

searches.

Figure 5: Fuzzy predicates configuration interface for deficient (deficiente), regular, good (buena), excellent (excelente)

and outstanding (sobresaliente) fuzzy predicates on the attribute score.

preferences.

As a database system with fuzzy logic is used,

queries might be more flexible and close to natural

language. Furthermore, system lists the results

obtained with their respective associated satisfaction

degree; so users might know how the result meets

the query criteria made, allowing among other things

enhance their search criteria for future queries.

Moreover, it would be very useful in decision-

making support.

The presented tool helps in exam configuration

based on the reutilization of questions according to

user preferences. This problem has been proposed

because it is in fact a real life need at the educational

institute IUT Federico Rivero Palacio.

Provided tool was built using the fuzzy querying

language SQLf. For the development, we use SQLfi.

That is a fuzzy logic layer implementing SQLf on

top of an existing relational DBMS. There are some

SQLf features that have not been used in this

application. We might improve it with such features.

It would also be useful to explore the need and

the way for the incorporation of vague information

in the database. SQLf does not currently support it,

so is matter of more research in future works.

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The application of fuzzy querying is relatively

new; therefore, it gives an added value to this

research area showing practical benefits.

This system shows the feasibility of building real

applications using fuzzy queries. As future work, we

will evaluate the acceptance of teachers in terms of

usefulness. To do this, we will do the steps for

implementing the system in the IUT Federico Rivero

Palacios at the academic department of informatics.

This evaluation should include how the teacher's

ability and experience affects the tests construction.

This allows validate the benefits expected from the

developed system.

Another future work is to evaluate the system

performance compared with other similar systems,

in the context of a significant database size. Since

SQLfi is developed as a logical layer on a relational

DBMS, it adds overhead time to the queries

response time. We have performed studies on the

time overhead resulting in some cases excessive. At

present time, we plan to develop a new version of

SQLfi to decrease the time overhead.

We hope to get a good system acceptance by

users. Thus, the database will grow to the extent of

their use, so it is essential to improve the

performance of SQLfi.

ACKNOWLEDGEMENTS

This work is supported in part by the Governmental

Venezuelan Foundation for Science, Innovation and

Technology FONACIT Grant G-2005000278. We

would also give thanks to the King of Kings who as

inspired us. We acknowledge that the intelligence

and the knowledge are gifts of Him Hands by Him

Love.

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