Quality testing at assessment of professional competencies in web learning management system
The article offers ways to improve the quality of testing in the assessment of professional competence in Learning Management System, including the reviewed system architecture and algorithm of its work. The proposed module of checking the criterion validity tests based on the analysis of indicators...
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Інститут проблем математичних машин і систем НАН України
2016
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nasplib_isofts_kiev_ua-123456789-1136682025-02-09T10:11:11Z Quality testing at assessment of professional competencies in web learning management system Якість тестування при оцінюванні рівня професійних компетенцій у веб-системі управління навчанням Качество тестирования при оценивании уровня профессиональных компетенций в веб-системе управления обучением Nikitenko, Ye.V. Trunova, O.V. Обчислювальні системи The article offers ways to improve the quality of testing in the assessment of professional competence in Learning Management System, including the reviewed system architecture and algorithm of its work. The proposed module of checking the criterion validity tests based on the analysis of indicators of asymmetry, kurtosis and discrimination index, set of these characteristics indicates on the validation methods that can increase the quality of testing. Designed module of providing and assessment of tests based on fuzzy sets allows you assessing accurately at the testing and thus pursue integrated account both of quantitative and qualitative factors. У статті пропонуються шляхи підвищення якості тестування при оцінюванні рівня професійних компетенцій у веб-системі управління навчанням, зокрема, розглянуто архітектуру системи та алгоритм її роботи. Запропонований модуль перевірки критеріальної валідності тестів ґрунтується на аналізі показників асиметрії, ексцесу та індексу дискримінації; сукупність наведених характеристик вказує на способи валідизації, що дозволяє підвищити якість тестування. Розроблений модуль надання та оцінки тестів на основі нечітких множин надає можливість достатньо точно виставити оцінку при проходженні тестування і при цьому проводити інтегральний облік як кількісних, так і якісних факторів. В статье предлагаются пути повышения качества тестирования при оценке уровня профессиональных компетенций в веб-системе управления обучением, в частности, рассмотрены архитектура системы и алгоритм ее работы. Предложенный модуль проверки критериальной валидности тестов основывается на анализе показателей асимметрии, эксцесса и индекса дискриминации; совокупность приведенных характеристик указывает на способы валидизации, что позволяет повысить качество тестирования. Разработанный модуль предоставления и оценки тестов на основе нечетких множеств дает возможность достаточно точно выставить оценку при прохождении тестирования и при этом проводить интегральный учет как количественных, так и качественных факторов. 2016 Article Quality testing at assessment of professional competencies in web learning management system / Ye.V. Nikitenko, O.V. Trunova // Математичні машини і системи. — 2016. — № 3. — С. 3–14. — Бібліогр.: 5 назв. — англ. 1028-9763 https://nasplib.isofts.kiev.ua/handle/123456789/113668 004.02:007 en Математичні машини і системи application/pdf Інститут проблем математичних машин і систем НАН України |
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Обчислювальні системи Обчислювальні системи |
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Обчислювальні системи Обчислювальні системи Nikitenko, Ye.V. Trunova, O.V. Quality testing at assessment of professional competencies in web learning management system Математичні машини і системи |
| description |
The article offers ways to improve the quality of testing in the assessment of professional competence in Learning Management System, including the reviewed system architecture and algorithm of its work. The proposed module of checking the criterion validity tests based on the analysis of indicators of asymmetry, kurtosis and discrimination index, set of these characteristics indicates on the validation methods that can increase the quality of testing. Designed module of providing and assessment of tests based on fuzzy sets allows you assessing accurately at the testing and thus pursue integrated account both of quantitative and qualitative factors. |
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Article |
| author |
Nikitenko, Ye.V. Trunova, O.V. |
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Nikitenko, Ye.V. Trunova, O.V. |
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Nikitenko, Ye.V. |
| title |
Quality testing at assessment of professional competencies in web learning management system |
| title_short |
Quality testing at assessment of professional competencies in web learning management system |
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Quality testing at assessment of professional competencies in web learning management system |
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Quality testing at assessment of professional competencies in web learning management system |
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Quality testing at assessment of professional competencies in web learning management system |
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quality testing at assessment of professional competencies in web learning management system |
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Інститут проблем математичних машин і систем НАН України |
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2016 |
| topic_facet |
Обчислювальні системи |
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https://nasplib.isofts.kiev.ua/handle/123456789/113668 |
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Quality testing at assessment of professional competencies in web learning management system / Ye.V. Nikitenko, O.V. Trunova // Математичні машини і системи. — 2016. — № 3. — С. 3–14. — Бібліогр.: 5 назв. — англ. |
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© Nikitenko Ye.V., Trunova O.V., 2016 3
ISSN 1028-9763. Математичні машини і системи, 2016, № 3
ОБЧИСЛЮВАЛЬНІ СИСТЕМИ
UDC 004.02:007
Ye.V. NIKITENKO
*
, O.V. TRUNOVA
*
QUALITY TESTING AT ASSESSMENT OF PROFESSIONAL COMPETENCIES IN
WEB LEARNING MANAGEMENT SYSTEM
*
Chernihiv National University of Technology, Chernihiv, Ukraine
Анотація. У статті пропонуються шляхи підвищення якості тестування при оцінюванні рівня
професійних компетенцій у веб-системі управління навчанням, зокрема, розглянуто архітектуру
системи та алгоритм її роботи. Запропонований модуль перевірки критеріальної валідності тес-
тів ґрунтується на аналізі показників асиметрії, ексцесу та індексу дискримінації; сукупність
наведених характеристик вказує на способи валідизації, що дозволяє підвищити якість тестуван-
ня. Розроблений модуль надання та оцінки тестів на основі нечітких множин надає можливість
достатньо точно виставити оцінку при проходженні тестування і при цьому проводити інтегра-
льний облік як кількісних, так і якісних факторів.
Ключові слова: веб-система управління навчанням, модуль перевірки валідності, модуль надання
та оцінки тестів.
Аннотация. В статье предлагаются пути повышения качества тестирования при оценке уровня
профессиональных компетенций в веб-системе управления обучением, в частности, рассмотрены
архитектура системы и алгоритм ее работы. Предложенный модуль проверки критериальной
валидности тестов основывается на анализе показателей асимметрии, эксцесса и индекса дис-
криминации; совокупность приведенных характеристик указывает на способы валидизации, что
позволяет повысить качество тестирования. Разработанный модуль предоставления и оценки
тестов на основе нечетких множеств дает возможность достаточно точно выставить оценку
при прохождении тестирования и при этом проводить интегральный учет как количественных,
так и качественных факторов.
Ключевые слова: веб-система управления обучением, модуль проверки валидности, модуль предо-
ставления и оценки тестов.
Abstract. The article offers ways to improve the quality of testing in the assessment of professional compe-
tence in Learning Management System, including the reviewed system architecture and algorithm of its
work. The proposed module of checking the criterion validity tests based on the analysis of indicators of
asymmetry, kurtosis and discrimination index, set of these characteristics indicates on the validation
methods that can increase the quality of testing. Designed module of providing and assessment of tests
based on fuzzy sets allows you assessing accurately at the testing and thus pursue integrated account both
of quantitative and qualitative factors.
Keywords: web learning management system, the module of checking the validity, module of providing
and assessment of tests.
1. Problem statement
At the present stage of development of Ukraine, it is essential to improve the system of traditional
university education. Creation and implementation of state standards of higher education require
further improvement and the search for effective forms, tools and training methods, new methods
of evaluating student achievement, creation an appropriate learning opportunities of students
learning model in higher education.
One way to improve the quality of the educational process in institutions of higher educa-
tion is the introduction of automated training system (ATS). ATS – a system that helps to master
new material performs control of knowledge and helps teachers in preparing teaching material.
4 ISSN 1028-9763. Математичні машини і системи, 2016, № 3
However, the results of such systems are perceived ambiguously, that hinders their widespread
use [1].
One of the aspects that should be considered in these systems is evaluation of level of pro-
fessional competencies that is the result of specific existing knowledge and skills, that is, what a
student should have at the end of the course. The elimination of subjectivity in the examination of
professional competence, promotes new advanced forms of electronic (computer) control (test-
ing), which is part of the ATS.
The current education system is characterized with intensive development of the theory
and practice of computer testing, which becomes an integral and important part of the educational
information environment of any educational institution.
Computer testing is a method of control, which is a standardized procedure of using tests
on a computer under a special software control that provides the required presentation of tests and
test results processing to solve complex task goals.
The most significant claim to the currently existing distance learning systems such as
Moodle, ILIAS, Web Tutor, CoureLab, Blackboard Learning System is a primitive control and
evaluation of test results.
From the mentioned above we can make a conclusion about feasibility of allocation of in-
dividual independent components in the architecture of the developing system. This will make
changes directly to individual modules without affecting the system as a whole.
The aim of article is to improve the quality of testing in the assessment of level of profes-
sional competence in Web-Learning Management System.
2. System architecture
The server of authorization processes the requests of unauthorized users to the system. Following
the link to the site in the browser, the user can enter a name and password for authentication.
Thanks to simple logic of work, the request to search a user in the system can be performed di-
rectly in the database. Depending on the type of user, a redirection takes place to one of the mod-
ules – test or viewing the results and quality of tests. A special key will be written to the brows-
er's session, without which further work with the system will be impossible.
Server of web-services contains a set of Web services that are required for the work with
database, services of providing and processing test results, also services of providing data about
their results and quality. Modules of passing tests and module of viewing their results and quality
will access data services.
The module of passing tests is a web-server that allows the passage of the tests through a
graphical Web interface. The results of passing the tests can be stored in the database by calling
web-service server with web services. Module of viewing the results and quality tests is a web-
server. It uses a graphical web interface and allows you to view the results of passed tests, that
stores in a database, as well as their quality.
When using the module of creation the graphs, it can visually learn the results. Control
Module of quality tests – is servers with applications that enable to call Web- services of pro-
cessing data and receiving results.
Module of competency assessment allows evaluating professional competence of stu-
dents.
The system architecture is shown in fig. 1.
ISSN 1028-9763. Математичні машини і системи, 2016, № 3 5
Fig. 1. System Architecture
3. The algorithm of the system
The procedure of detection of professional competencies in automated mode is implemented in
two phases: the first phase includes the formation of test for students (from a set of test's tasks). It
consists of two parts:
1. Experts offer a set of questions of custom test.
2. The test is verified on quality (validity and reliability).
2.1. Construction and analysis of matrix of results.
2.2. Determination of complexity and item determination of tests.
2.3. Determination of variation of marks (difference of maximum and minimum dialed
scores), asymmetry, kurtosis and discrimination index).
Based on the questions that have been passed quality control, the base of the questions of
user test is made, thus questions are divided into categories for the evaluation of a set of profes-
sional competencies.
The second phase involves obtaining the final test result in dependence on the importance
of specific competence for individual student after passing user’s test.
Block diagram of previously given process is shown in fig. 2.
4. The module of the validity checking
The validity in theory of testing means compliance of form and content of the test to that it has to
evaluate or measure on a plan of its creators.
Validity – one of the main criteria for a quality of test, the degree of coverage by the test
the set of objects subject area compared with the standard. Validation – is not about gathering
evidence of validity of the test, but about the process of actions, that raise its validation. As a re-
sult, the evidence base of the validity of the test will grow.
In the ATS the following analysis scheme of criteria l validity (provides the availability of
external criterion – standard, which defines the validity of test) test’s tasks are offered, that are
based on the results of statistical data of testing with building a curve of distribution of correct
answer’s number on specific task of test. Not valid tasks considered those tasks, which had been
received during the testing the following statements the right answers more than 84% of tested
persons; correct answers less than 16% of tested persons.
6 ISSN 1028-9763. Математичні машини і системи, 2016, № 3
Fig. 2. Block diagram of the algorithm of the system
Distributional (discriminatory) ability of the test task defines by the index (discrimination
index), which indicates to what extent the results obtained in the performance of individual test
tasks correlate with the results of all test and how well it’s test task distinguishes tested persons
with high mark and tested persons with low mark.
Distributional ability can be calculated as the correlation coefficient between the mark for
whole test and a mark for a particular test task. The correlation coefficient is calculated as fol-
lows:
,
1
( )x y B H j j
x
r X X p q
s
,
where TX – arithmetic mean score of tested persons, that successfully completed the j -th task
of test, NX – the arithmetic mean score of tested persons, that not coped with the j -th task,
jj qp – standard deviation of the j -th task, xS – standard deviation throughout the test. The
value of the correlation coefficient is interpreted as follows: 0,7–1 – the relationship is very
strong; 0,5–0,7 – average; 0,3–0,5 – weak.
The index can range from -1 to +1. When the index is «0», it means that all tested persons
responded the same way (good or bad). If the correlation is positive (higher than 0), it is a suffi-
ISSN 1028-9763. Математичні машини і системи, 2016, № 3 7
cient discriminatory ability of this test, it means that it has the sufficient ability to determine
strong and weak of the tested persons. It is believed that difficult tasks have to be solved success-
fully by more prepared tested persons and easy tasks – less prepared, but good prepared also. If
test results show a different picture, this test does not meet the characteristics and needs high-
quality processing.
General characteristic of test for validity provides comparisons of indicators of asym-
metry, kurtosis and discrimination index see table 1, which is directly implemented in the system.
Table 1. Analysis of tests for validity
As Ex
yxr , Characteristic Methods of validation
= 0 = 0 The valid test
< 0 > 0 0,3-0,5 easy test with low resolution to complicate the task
to increase the resolution of
problems
improve administration of proce-
dure testing
< 0 < 0 0,7-1 easy test with high resolution to complicate the task
< 0 = 0 easy test to complicate the task
> 0 < 0 0,7-1 complex test with high-resolution remove or simplify tasks that
make testing difficult
> 0 > 0 0,3-0,5 complex test with low-resolution validation of tasks
> 0 = 0 complex test validation of tasks
= 0 > 0 0,3-0,5 heterogeneous test (presence of
heavy and light tasks) with low reso-
lution
validation of the test is the
division into two separate tests.
= 0 < 0 0,7-1 heterogeneous test (presence of
heavy and light tasks) with high reso-
lution
validation of the test is the
division into separate tests.
5. Module of provision and assessment of tests
Getting the final test result has the goal to objectively assess the level of training of students, in
particular to reduce the subjectivity that appears between teachers and students, and eliminate
other (probably hidden) factors that interfere. Evaluation of the test task is that the student accu-
mulates points for the test, and then total result is translated into all the usual estimate.
But, first, various questions from the proposed list obviously are measured in different
units, and therefore the direct storage system is problematic. Second, as a rule, the translation
method of total result into the assessment remains outside of using fuzzy logic [2], or applies the
standard approach of working with random variables [4].
When building a model of formation of linguistic evaluation of student’s competences, in
our view, as input variables should be used as a quantitative factors (Q – number of questions,
kb – the number of correct answers, – total score), and qualitative factors (
1x – mastering
level on 2 points. (unsatisfactory);
2x – level – on 3 (satisfactory);
3x – level – on 4 (good);
4x –
level – on 5 (excellent)) [2].
Model of linguistic assessment of competence of the student (F) in general can be repre-
sented as follows (fig. 3).
8 ISSN 1028-9763. Математичні машини і системи, 2016, № 3
Fig. 3. The formation model of linguistic assessment of competence level, where S – the sum of
points obtained by the student, 41 xx – the level of assessment
The algorithm of formation of linguistic competence assessment of students is as follows:
1. To carry out the rationing of the accumulated amount of points accumulated on the in-
terval [0;100] by levels.
Determine auxiliary variables іх , 1,4i normalizing the accumulated amount of points
on the interval [0;100] regardless of the number of questions in the test:
1 1 1
5 10
(2 (int( ) int( )) int( )),
15 15 15
Q Q Q
х S k k
);02mod(),1
10
()23(
);02mod()),5,1
10
(2)
10
(int(3(
11
11
2
QpairQif
Q
kkS
QunpairedQif
Q
k
Q
kS
х
);02mod(),1
10
()32(
);02mod()),5,1
10
(3)
10
(int(2(
22
22
3
QpairQif
Q
kkS
QunpairedQif
Q
k
Q
kS
х
3
4
3
( ( 2) 1)
,
10
(( ( 2) 1) 4 ))
5
S k Q
х
Q
k Q
1 1 2 2 3 3,S k b k b k b
where S – cumulative total score;
Q – the number of questions in the test, at 10Q , Qmod 2=0;
1b – the number of correct answers to the difficulty level 1;
2b – the number of correct answers to the difficulty level 2;
3b – the number of correct answers to the difficulty level 3;
1k – coefficient for the correctness of answer to questions of 1 level of complexity;
2k – coefficient for the correctness of answer to questions of 2 level of complexity;
3k – coefficient for the correctness of answer to questions of 3 level of complexity, at
3210 kkk ;
int – division function that returns an integer;
mod – division function that returns the remainder.
The variable іх , 4,1i (
1x – unsatisfactory level;
2x – satisfactory level;
3x – good lev-
el;
4x – excellent level) standardized depending on meanings of the auxiliary variable
ix on in-
terval [0;100]:
ISSN 1028-9763. Математичні машини і системи, 2016, № 3 9
.4,7,100
,4,74,5),4,7(3100
,4,54,3),4,3(5,474
,4,37,2),7,2(4,1166
,7,22),2(8,1257
,26,1),6,1(2049
,6,12,1),2,1(5,6224
,2,1,20
i
ii
ii
ii
ii
ii
ii
ii
і
xif
xifx
xifx
xifx
xifx
xifx
xifx
xifx
х
Formulas compiled in accordance with the 100-point scale that used to convert points into
traditional assessment and numeric value
ix , 1,4i obtained in the experiment (see table 2).
Table 2. The numerical values necessary for the formation of the argument x of membership
functions of quality of output variable
Transi-
tion
coeffi-
cient
0 1,2 16 2,0 2,7 3,4 5,4 7,4
The nu-
merical
value of
member-
ship
function
20 62,5 20 12,8 11,4
4,5
Total
points for
all types
of the
educa-
tional
activities
0 24 49 57 66 74 83 100
Evalua-
tion by
national
scale
1x – unsatisfactorily
2x – satisfacto-
rily
3x – good
4x –
ex-
cellent
Assess-
ment
ECTS
F FX E D C B A
For example, the value 7,4 corresponds to the maximum value
4x at the passage of the
adaptive test which begins from 1 level of complexity; values 3,4; 2 and 1,2 – the average value
3x ,
2x ,
1x , the values 1,6; 2,7 and 5,4 obtained by adding the value of the left value and the dif-
10 ISSN 1028-9763. Математичні машини і системи, 2016, № 3
ference between neighboring values divided in half (1,6=1,2+(2–1,2)/2; 2,7=2+(3,4–2)/2, and so
on. d.).
On each of the received segments there were compliance coefficients of numerical values
to marks. For example, between [3,4; 5,4] are points from 74 to 83, that’s why a numerical value
on this interval corresponds to 4.5 points ((83–74)/(5,4–3,4)).
2. Write the classification scale of linguistic variables
ix , 4,1i Linguistic variables:
1x
– level of 2 points (unsatisfactory);
2x – level – of 3 (satisfactory);
3x – level – of 4 (good);
4x –
level – of 5 (excellent), interpreting as a term-set with three-dimensional scale Т2={NL, CS,
CC}, where value NL – «does not correspond to the level of (2, 3, 4 or 5)», CS – «corresponds to
slightly» and CC – «completely corresponds».
3. Ask a membership function of quality of variables
ix , 4,1i .
Each of linguistic variables «level on 2 (3, 4, 5)» has one triangular curve of membership
(1) and two T-curves of membership (2) ( , ,
і і і
NL CS CC
x x x
, 1,4i ), that in general may be given
by expressions:
0, ,
, ,
( , , , )
, ,
0, ,
j
і
x a
x a
a x b
b a
х а b c
c x
b x c
c b
c x
(1)
where 1,4i ; { 2}j T ; , ,a b c – some numerical parameters that characterize base of the tri-
angle ,a c and its top (b), moreover, the condition a b c must be met.
0, ,
, ,
( , , , , ) 1, ,
, ,
0, ,
j
T і
x a
x a
a x b
b a
х а b c d b x c
d x
c x d
d c
d x
(2)
where 1,4i ; { 2}j T ; , , ,a b c d – some numerical parameters that characterize the lower
base of the trapezoid ,a d and the upper base of the trapezoid ,b c , that acquire arbitrary
real values and organized by ratio: a b c d .
With considering (1) and (2) the membership function of fuzzy-term set of linguistic vari-
able «level 2 (3, 4, 5)»
1 2 3 4, , ,x x x x will be as follows:
1)
1
NL
Т x
(
1x , 0, 0, 15, 30);
CS
x1
(
1x , 20, 35, 50);
CC
x1
(
1x , 40,55,100, 100);
2)
NL
xТ 2
(
2x , 0, 0, 20, 40);
CS
x2
(
2x , 30, 50, 70);
CC
x2
(
2x , 60, 70, 100, 100);
3)
NL
xТ 3
(
3x , 0, 0, 50, 70);
CS
x3
(
3x , 55, 70, 85);
CC
x3
(
3x , 70, 90, 100, 100);
4)
NL
xТ 4
(
4x , 0, 0, 70, 80);
CS
x4
(
4x , 70, 80, 90);
CC
x4
(
4x , 80, 90, 100, 100).
ISSN 1028-9763. Математичні машини і системи, 2016, № 3 11
Implementation of vague conclusions is based on logic algorithm of Mamdani [Mamda-
ni], because the problem is solved of gaining knowledge from data (in the form of linguistic
rules). The algorithm works like a «black box». The input received numerical value, is the output
[3].
The parameters a, c, b, d can be adjusted according to experimental data.
When using classifiers (triple and quaternary scales) on the carriers of fuzzy set the im-
portance of linguistic variables is defined on the real axis interval [0; 1].
4. Determine the classification scale and membership functions of quality of investigated
parameter (output variable) «Evaluation of student competencies» as a term-set of values
T1={unsatisfactory (U), satisfactory (S), good (G) excellent (E)}. Linguistic variable «Evaluation
of student competencies» has two triangular curves of membership and two T-shaped curves of
membership , , ,U S G E
F F F F
.
With considering (1) and (2) the membership functions of fuzzy-term set of linguistic var-
iable «Evaluation of student competencies» (F) will have the following form:
U
F ( x , 0, 0, 25,
50);
S
F ( x , 40, 55, 70);
G
F ( x , 60, 75, 90);
E
F ( x , 80, 90, 100, 100).
5. Identify the knowledge base that needed to set fuzzy production rules of assessment of
student competencies is formed by specialists of subject area. It is as follows (table 3).
Table 3. Fuzzy production rules
Rules View term Variable
F
1х
2х
3х 4х
PR1 NL +
F
E
– excellent CS + + +
CC +
PR2 NL + F
G
– good
CS + +
CC +
PR3 NL + F
G
– good
CS +
CC + +
PR4 NL F
G
- good
CS +
CC
PR5 NL + + F
S
– satisfactory
CS + +
CC +
PR6 NL + + + F
S
– satisfactory
CS + +
CC
PR7 NL + + + F
U
– unsatisfac-
tory
CS +
CC +
PR8 NL + F
U
– unsatisfac-
tory CS + +
CC +
12 ISSN 1028-9763. Математичні машини і системи, 2016, № 3
In our view, at the development of competency level assessment module the most appro-
priate is integration already existing in environment MATLAB package of Fuzzy Logic Toolbox
in created software addition. Fig. 4–5 shows windows of the editor of membership functions and
the editor of fuzzy inference rules of membership of environment MATLAB Fuzzy Logic
Toolbox, where alternatively defined membership functions for each of the terms of the input and
output variables.
Fig. 4. The editor of membership functions
Fig. 5. Rule Editor of fuzzy inference system
ISSN 1028-9763. Математичні машини і системи, 2016, № 3 13
6. Accumulation of conclusion by all rules carried out using max-disjunction operation.
The main methods of defuzzification (transformation of fuzzy set of findings in a clear number)
are a lot of methods of reduction to the definition: the method of selecting a maximum of mem-
bership function; method of center of gravity; median method; the method of choosing center of
highs etc.
Experimental studies prove that the most accurate – is a method of center of gravity (see
fig. 6) for a discrete set of values of membership functions
max
max
1
1
( )
( )
f
r Б r
g r
f
Б r
r
f f
f
f
,
where
maxf – number of elements in
rf sampled for calculation of the «center of gravity» area F
[5].
Fig. 6. Rules viewer program
7. Evaluation of student mastering level of competencies derives from the maximum
amount of points that equal to 100 [4]. If each of level of test’s tasks nFFF ,...,, 21 is known a lin-
guistic evaluation ))(),...,(),(()( 21 xxxx n and the weight's coefficients were defined
1 2( , ,..., )
n
p p p p , then the operator of information’s aggregation is the weighted sum and is
characterized by its linguistic assessment, that defined by membership function on 01-classifier
1
( ) ( )
n
F i i
i
x x p .
It should be noted that the results of the testing (evaluation of mastering level of student's
competence) can be used for future monitoring of professional’s level of competence and check-
ing validity as a reference.
14 ISSN 1028-9763. Математичні машини і системи, 2016, № 3
6. Conclusions
The results of approbation of created Web system, whose main function is to automate the pro-
cess of determining the level of formation of professional competence of graduates in each of the
selected areas indicate the reliability and efficiency of adaptive testing environment. Simplicity,
adaptability and versatility of the software complex allow to use it for either examination of pro-
fessional competence or to assess knowledge of the discipline.
It was experimentally established that using the algorithm of formation the linguistic
evaluation of student’s competences based on vague sets allows you to accurately enough set the
assessment at the testing and thus pursue integrated account both quantitative (number of ques-
tions, the number of correct answers, total score) and qualitative factors (
1x – the level of assimi-
lation on 2 (unsatisfactory),
2x – level on – 3 (satisfactory),
3x – level on – 4 (good),
4x – level
on – 5 (excellent), considering the uncertainty of the last. By installing criterion significance of
reliability’s level of membership functions of input (output) variable’s quality, you can change
the final results depending on the level of preparedness of students.
The developed application has the following advantages: learning regardless of time and
spatial location; checking tests for the validity allows teachers to choose more quality educational
material and does not go beyond student’s knowledge level; the ability to view material not just
as text, but also through media increases the effectiveness of training program; the ability to view
test results and demonstration of statistics as graphs are more clearly.
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Стаття надійшла до редакції 07.06.2016
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http://www.ict.edu.ru/%20vconf/files/tm01_627.doc
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