Nia Amelia et al. Meta-analysis of Student Performance Assessment...| 74
Indonesian Journal of Science, and Technology 4 (1) (2019) 74-88

Indonesian Journal of Science, and Technology
Journal homepage: http://ejournal.upi.edu/index.php/ijost/

Meta-analysis of Student Performance Assessment Using
Fuzzy Logic
Nia Amelia, Ade Gafar Abdullah, Yadi Mulyadi
Program Studi Pendidikan Teknologi dan Kejuruan, Sekolah Pascasarjana Universitas Pendididikan Indonesia,
Jl. Dr. Setiabudhi No. 229 Bandung, Indonesia


Correspondence: E-mail: niaamelia@upi.edu

ABSTRACT
The assessment system generally requires transparency and
objectivity to assess student performance in terms of
abstraction. Fuzzy logic method has been used as one of the
best methods to reduce this uncertainty. Therefore, we have
conducted a literature review to examine the application of
fuzzy logic in assessing the performance of different
students. The Preferred Reporting Items for the Systematics
Review and Meta-Analysis Method (PRISMA) were used as
the basic method for conducting systematic reviews and
meta-analyses. The articles reviewed were 38 articles from
2008 until 2018. All articles were classified based on the
author, year of publication, type of journal or conference,
sample size, context, data type, fuzzy technique, and basic
findings. The results of this review show the positive effects
of using fuzzy logic on student performance assessment.
Overall, this review provides an appropriate reference for
further research by identifying research needs in aspects of
student performance assessment.

ARTICLE INFO
Article History:
Submitted/Received 09 Nov 2018
First revised 31 Jan 2019
Accepted 02 Mar 2019
First available online 09 Mar 2019
Publication date 01 Apr 2019

____________________
Keywords:
Meta-analysis,
Student Performance
Assessment,
Fuzzy Logic,
Fuzzy technique.

© 2019Tim Pengembang Jurnal UPI

1.

INTRODUCTION

Student performance assessment is a
process in determining the level of performance for each student related to educational goals (Saleh and Kim, 2009). The assessment process for students is based on
several elements such as examinations, assignments, tests, quizzes, research work, etc.

(Daud et al., 2011). Assessments must be carried out in a more impartial and transparent
manner. Assessment is usually subjective,
which can lead to differences of opinion in assessing student performance (Ingoley and
Bakal, 2012). The assessment system must be
regularly reviewed and improved to ensure
that the assessment is appropriate, impartial
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and beneficial to all students. Therefore, the
assessment system requires transparency,
objectivity, logical reasoning, and easy computer implementation to assess student performance in terms of abstraction (Henriques
et al., 2018).
The education system in general usually
uses classical performance assessment methods. Based on this system, student performance assessment depends on the results of
the exam and is evaluated only as a factor of
success or failure. As an alternative, non-classical performance assessment methods can
be used, such as fuzzy logic can be applied to
various forms of decision making including
research on techniques and artificial intelligence (Barlybayev et al., 2016; Gokmen et al.,
2010; Yadav and Singh, 2011). Fuzzy logic is
used to manage intrinsic uncertainty associated with teacher subjective judgments and
allows modeling of students in linguistic form
(Darwish, 2016). This model approach provides various perspectives from different
people, prevents subjectivity in the evaluation process, and allows students to take an
active role in assessment. The software developed for the specific purpose of this study
is user-friendly and has multi-criteria decision dimensions (Baba et al., 2012). Based on
this, it can be said that fuzzy logic can be used
to solve problems in assessing student performance.
Student performance assessment using
fuzzy logic is a research topic that has been
carried out for many years and is an application used to facilitate assessment in the education system. In this paper, a literature review on student performance assessment using fuzzy logic from 2008 to 2018 is presented
along with views in providing summaries and
criticisms of the topics and research methods
used until now. We can use this information
to identify and answer problems or objectives in the literature and determine research
directions on assessing student performance
using fuzzy logic for further research.

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The rest of the article is organized as follows. First, the relevant theoretical framework of this study provides a brief description of student performance assessment and
fuzzy logic. Next, the report on how to search
and choose the relevant literature study, and
how this research is analyzed in the method
section. Finally, the identified research topics
and their related findings are reported in the
results section, this is followed by the discussion and conclusion of the review.
2.

THEORETICAL FRAMEWORK

2.1 Student performance assessment
Performance evaluation is one of the bases for monitoring the progress of student
performance in higher education institutions.
Based on this critical problem, grouping students into different categories according to
their performance has become a complicated
task. By using traditional grouping of students based on average scores, it is difficult
to get a comprehensive view of the state of
student performance and simultaneously
find important details of student performance over time. The ability to monitor the
progress of student academic achievement is
an important problem for teachers for higher
learning (Johanyák 2010; Oyelade et al.,
2010). Student performance assessment usually consists of several components, each involving a number of assessments often based
on inappropriate data. This inaccuracy arises
from the teacher's interpretation of student
performance (Yadav et al., 2014).
2.2 Fuzzy logic
Fuzzy logic was introduced in 1965 by
Lotfi Zadeh as a mathematical way to represent linguistic uncertainty (Sakthivel et al.,
2013; Zadeh, 1965). The basis of fuzzy logic is
fuzzy set theory. In fuzzy set theory, the role
of membership degrees as a determinant of

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the existence of elements in a set is very important. The membership function (mf) is the
main feature of reasoning with the fuzzy logic
(Jafari and Khotanlou, 2013; Kusumadewi
and Purnomo, 2010). Fuzzy logic generally
has three stages. Fuzzification is a process
where the actual value is an input in the system. Each input value is rated membership
and transforms into linguistic forms. The second stage is giving algorithm rules. Input is
adjusted to the rule table. The third stage is
defuzzification, involving the transformation
of fuzzy values to actual values (Yildiz et al.,
2013). In the past decade, fuzzy logic has not
only been used in engineering and science
but has also been widely applied in education
(Sripan and Suksawat, 2010).
2.3 Student performance assessment using
fuzzy logic
Student performance assessment involves measuring abilities, competencies and
skills. Ability, competency and skills are fuzzy
concepts and can be captured in fuzzy form.
This usually involves giving numerical values
to students who represent their achievements by reasoning using arithmetic or statistical methods. Various criteria are used in
mathematical methods for evaluation purposes (Arora and Saini, 2013). According to
fuzzy logic, factors and criteria can be classified without certain limits. Thus, the application of fuzzy logic models can be done with a
more flexible student learning evaluation
model. This also works for flexible non-homogeneous data and objective performance
evaluations, which is reported by Gokmen et
al. (2010). Evaluation of student performance assessment using fuzzy logic has great
flexibility and reliability. It is proven theoretically but can be an integral part of decision

making and evaluation processes in educational institutions, as well as influential in improving the quality of educational facilities,
motivation, reliability, consistency, and assessment objectivity. Several reports have
been reported, such as Baba et al. (2012),
Bhatt and Bhatt (2011), Voskoglou (2012),
and Inyang and Joshua (2013).
3. METHOD
This literature review is carried out
based on the PRISMA (Preferred Reporting
Items for Systematic Reviews and Meta-Analysis) method, which was introduced by
Moher et al. (Moher et al., 2009). PRISMA is
one of the best methods that can help the author to carry out systematic reviews and
meta-analyses correctly and also help author
to review a structure like a road map. PRISMA
is also the most commonly used method in
articles such as literature reviews, such as
Moher et al. (2009, 2010, 2015), Hutton et al.
(2016), Shamseer et al. (2015), and Stewart
et al. (2015). Systematic review is a valuable
source of evidence, where the author must
summarize and analyze the scientific literature that can be relied upon by using a structured procedure based on the objectives that
have been determined so that the different
researchers can use them as reported by Gopalakrishnan and Ganeshkumar (2013). In addition, this review must also define qualified
criteria so that they can be carefully selected
to describe the hypothesis (Ressing et al.,
2009). This can be an important role in solving problems by explaining, synthesizing, and
assessing quantitative or qualitative evidence as reported by Ahmadi et al. (2018).

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3.1 Search strategies
In this stage, we choose scientific articles
related to student performance assessment
using fuzzy logic published in journals indexed in the SCOPUS database (60) and
Google Scholar (64900). Other searches were
carried out through the publisher databases
of Elsevier (2093), Emerald (1507), Hindawi
(60), IEEE (16), IET Journals (82), Routledge or
Taylor, and Francis Online (6275), Springer
(66553), and Wiley Online Library (6076). Literature search is done using keywords including "performance assessment", "student
performance assessment", "fuzzy logic" and
"student performance assessment using
fuzzy logic". These articles were searched
from January 2008 until October 2018. Based
on the search strategy, 154 articles were
taken and put into citation management software such as Mendeley. By doing the PRISMA
method, the next step is the elimination of
duplication automatically by the software.
Duplication remove is done, where the same
12 articles have been removed. Finally, 142
articles remained after the stages were carried out.
3.2 Inclusion and exclusion criteria
Eligibility criteria are needed in selecting
the appropriate article such as reported by
Ahmadi et al., (2018). Articles are filtered

based on inclusion and exclusion criteria described in Table 1. According to exclusion criteria, articles that meet the requirements are
selected, but for chapter book types, thesis,
short reports, non-empirical study or articles,
and non-English papers have been removed.
In other words, only international conference
proceeding and journals paper are considered in accordance with the inclusion criteria.
In this case, 17 articles that did not match
were removed and the article became 125 articles. The screening of articles was carried
out by investigating the title and abstract
based on the relevance of the subject of the
article related to the student performance
assessment using fuzzy logic. During this
stage, irrelevant articles have been removed.
Overall, there are 38 articles that match the
inclusion criteria and relevant to this literature review research objectives.
3.3 Data extraction and analysis
In the final step, the 38 full text articles
that would contribute to this literature review are examined. Articles are thoroughly
examined to extract and summarize important information needed in order to answer the research objectives in this literature
review. Based on the information needed,
we consider several classifications and criteria that are in accordance with the objectives.

Table 1. Inclusion and exclusion criteria.
Inclusion Criteria

Exclusion Criteria

Language: English

Articles written in other languages

Time: January 2008 until October 2018

Before year 2008

Empirical research published through interna- Chapter book types, thesis, short reports,
tional conferences and international journals non-empirical study or articles.
Related to the student performance assess- All scientific disciplines except in student
ment using fuzzy logic
performance assessment using fuzzy logic

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Nia Amelia et al. Meta-analysis of Student Performance Assessment...| 78

Data extraction is designed to classify,
analyze and synthesize articles that meet the
requirements based on specified criteria.
Then, based on the analysis of data extraction, we can achieve the best results and recommendations. The criteria are the author,
year of publication, type of article, journal or
conference, sample size, context, data type,
fuzzy technique, and basic findings. After reviewing and summarizing the articles collected, 38 articles from 26 international scientific journals and 12 conferences from
2008 to 2018 were matched with the inclusion criteria recognized as suitable articles to
be analyzed and interpreted in this literature
review. The whole texts were read, and the
details for selecting articles related to the application of fuzzy logic in student performance assessment were taken into account.
However, it is still carried out in accordance
with the PRISMA method although choosing
the right article requires a lot of time but because of its special structured nature of this
method, it was sure that the most appropriate and relevant articles related to the subject of this literature review have been selected.
4

RESULTS

Findings from the results of analysis
and synthesis of related articles are presented in this section. Based on systematic reviews and the results of meta-analysis data, various fuzzy logic methods
that have been applied in student performance assessment are found out. Therefore, studies that fulfill the requirements
regarding the application of fuzzy logic
methods used in student performance
assessment are summarized and classified as follows. This is based on various
categories considered in relation to the
research objectives.

4.1. The frequency of articles published
during the last several year
The articles reviewed in this literature review were 38 articles that met the
inclusion criteria. The article included
24.68% of the total articles taken
through database searches based on keywords and year the article was published
in the first step. The frequency of articles
published in the period between 2008
and 2018 is shown in Figure 1. The graph
shows that there was a significant increase in articles published from 2008 to
2018. In this case, almost 23.68% of articles were published in 2013. However,
based on the graph below, it can be seen
that there was a decrease interest in
2014 and 2015. This is evidenced by the
little research that has been done. Even
in 2017 there are no articles that discuss
more specifically related to the application of fuzzy logic in student performance assessment. On the other hand,
significant progress has been made in
several years with achieving about six articles in 2012, nine articles in 2013, and
seven articles in 2016. Therefore, based
on these results it is clear that increasing
interest in applying the fuzzy logic
method in student performance assessment is presented.
4.2. Articles distribution based on international journal and conference type
Reviewed articles in this literature review were taken from 38 different international scientific journals and international
conferences in 2008 until 2018. The distribution of articles based on of publication type is
presented in Figure 2.

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23,68%

25,00%

18,42%

Frequency of Articles

20,00%
15,00%

15,79%

10,00%

10,53%

0,00%

7,89%

7,89%

5,00%

5,26%

5,26%
0,00%

2,63%

2,63%

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Year(s)

Figure 1. Frequency of articles based on publication year.

Total of Articles

7
6
5
4
3
2
1
0
Journal
Conference

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
0
2
1
3
6
7
0
1
5
0
1
1

1

2

1

0

2

2

1

2

0

0

Year (s)

Figure 2. Articles distribution based on international journal and conference type.
As shown in the Figure 2, articles published in international scientific journals are
significantly more dominant than articles
presented in international conferences. This
was shown in 2012 reaching six international
scientific journals publication, but there were
no articles presented at international conferences.
4.3. Articles distribution based on sample
size
In this section, the sample size used in
various articles has been surveyed. In this
survey, researchers found a minimum sample size of nine samples in one of the articles
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published in 2015 (Prokhorov and Kulikovskikh, 2015), and the maximum sample size
was 1121 in one of the articles published in
2008 (Rusli et al., 2008).
The most widely used sample average
was 20 samples from 7 articles reviewed,
such as Gokmen et al. (2010), Yadav and
Singh (2011), Darwish (2016), Yadav et al.
(2014), Sakthivel et al. (2013), Bhatt and
Bhatt (2011), and Inyang and Joshua (2013).
Based on these findings, we also found that
three out of 38 articles did not explain explicitly the number of sample sizes in the article,
such as Ingoley and Bakal (2012), Owais

Nia Amelia et al. Meta-analysis of Student Performance Assessment...| 80

(2009), and Do and Chen (2013). The distribution of articles based on sample size is more
clearly shown in Figure 3.
4.4. Articles distribution based on context
In articles distribution based on this
aspect, it is found several uses of fuzzy logic
in the academic field such as:

•
•
•
•
•
•

behavior (Vesely et al., 2016),
e-learning (Herrera-Viedma et al., 2009;
Dias and Diniz, 2013; Almohammadi and
Hagras, 2013; Deborah et al., 2015;
Arguedas et al., 2018),
intelligent tutoring system (Chrysafiadi
and Virvou, 2012; Machado et al., 2016),
laboratory application (Gokmen et al.,
2010; Yıldız and Baba, 2014),
library service quality (Jamali and
Sayyadi Tooranloo, 2009),
presentation skills (Daud et al., 2011;
Ozdemir and Tekin, 2016),
problem solving (Voskoglou, 2012;
Voskoglou, 2013; Owais, 2009),
student academic performance (Ingoley

•

•

Based on these findings, it turns out that
the application of fuzzy logic is numerous
and in various aspects, especially in terms of
predicting student academic performance.
The distribution of articles based on context
is shown in Figure 4.

2018
2017
2016

Article 8

2015

Year(s)

•
•

•

and Bakal, 2012; Yadav and Singh, 2011;
Oyelade et al., 2010; Yadav et al., 2014;
Sakthivel et al., 2013; Yildiz et al., 2013;
Arora and Saini, 2013; Inyang and
Joshua, 2013; Rusli et al., 2008; Do and
Chen, 2013; Hidayah et al., 2013; Borkar
and Rajeswari, 2013),
student learning assessment (Sripan and
Suksawat, 2010),
student
performance
assessment
(Barlybayev et al., 2016; Darwish, 2016;
Baba et al., 2012; Bhatt and Bhatt, 2011;
Prokhorov and Kulikovskikh, 2015; Patil
et al., 2012; Pöysä-Tarhonen et al.,
2016), and
students' choice (Henriques, et al., 2018;
Kabra and Bichkar, 2011).

Article 7

2014

Article 6

2013

Article 5

2012

Article 4

2011

Article 3

2010

Article 2

2009

Article 1

2008
0

200

400

600

800

1000

1200

Total of Sample Size

Figure 3. Articles distribution based on sample size.

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5%

3%

Behavior
13%

E-learning

18%
5%

Intelligent Tutoring System
Laboratory Application

5%

Library Service Quality
Presentation Skill

3%
3%

Problem Solving

5%

Student Academic Performance
Student Learning Assessment

8%
32%

Student Performance Assessment
Students' Choice

Figure 4. Articles distribution based on context.

4.5. Articles distribution based on data
type
Based on the data type in each article reviewed, it was found several methods that
are often used in this study such as:
• case studies (Machado et al., 2016),
• comparative analysis (Daud et al., 2011;
Ingoley and Bakal, 2012; Barlybayev et
al, 2016; Gokmen et al., 2010; Yadav and
Singh, 2011; Darwish, 2016; Oyelade et
al., 2010; Sakthivel et al., 2013; Bhatt
and Bhatt, 2011; Rusli et al., 2008;
Ozdemir and Tekin, 2016; Owais, 2009;
Do and Chen, 2013),
• experiment (Arora and Saini, 2013;
Prokhorov and Kulikovskikh, 2015;
Herrera-Viedma et al., 2009; Dias and
Diniz, 2013; Almohammadi and Hagras,
2013; Deborah et al., 2015; Arguedas et
al., 2018; Voskoglou, 2013; Borkar and
Rajeswari, 2013),
• mix methods (Henriques et al., 2018;
Baba et al., 2012; Yadav et al., 2014;
Yildiz et al., 2013; Sripan and Suksawat,
2010; Voskoglou, 2012; Inyang and

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Joshua, 2013; Vesely et al., 2016;
Chrysafiadi and Virvou, 2012; Patil et al.,
2012; Pöysä-Tarhonen et al., 2016; Kabra
and Bichkar, 2011), and
• questionnaire (Yıldız and Baba, 2014;
Jamali and Sayyadi Tooranloo, 2009;
Hidayah et al., 2013).
The method often used in research using
fuzzy logic is more directed towards comparative analysis methods, where the study
compares the use of fuzzy logic with classical
models. The distribution of articles based on
data type is presented in Figure 5.
4.6. Article distribution based on fuzzy
technique
Based on fuzzy technique in the literature review on articles from 2008 until 2018,
they are very diverse, especially the application in assessing student performance. There
are several fuzzy techniques that are often
used in student performance assessment
such as FST, FRB, FIS, and FES. The distribution of articles included based on fuzzy techniques is presented in Figure 6.

Nia Amelia et al. Meta-analysis of Student Performance Assessment...| 82

8%

3%
34%
Case Study
Comparative Analysis
Experiment

31%

Mix Methods
Questionnaire

24%

Figure 5. Articles distribution based on data type.

9

Fuzzy Set Theory (FST)

Total of Fuzzy Technique

8

Fuzzy Rules Based (FRB)

7
Fuzzy Neural Network (FNN)
6
Fuzzy Information Retrieval (FIR)
5
Fuzzy Inference System (FIS)

4
3

Fuzzy Expert System (FES)

2

Fuzzy Decision Tree (FDT)

1

Fuzzy Decision Support System
(FDSS)

0

Fuzzy Cognitive Map (FCM)

Year(s)

Fuzzy Classification (FC)

Figure 6. Articles distribution based on fuzzy technique.
4.7. Article distribution based on basic
findings
Basic findings that were examined from
this literature review are whether or not the
fuzzy method used can have a positive effect
on student performance assessment. Therefore, the reported effects of the fuzzy
method on student performance assessment

based on the different articles surveyed in
this review were examined. The results obtained that almost 97.37% of articles illustrate the positive effects of the fuzzy methods application in improving student performance assessment. The article that illustrate
the negative effects of the application of
fuzzy methods because the results obtained
do not make the significant changes due to a
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decrease in some student scores (Ozdemir
and Tekin, 2016). However, this result is limited to only 38 articles selected in this review.
To show how far the application of fuzzy
methods, they have shown a positive effect
on student performance assessment. The effect of fuzzy methods according to each fuzzy
method in terms of student performance assessment is shown in Figure 7.
5. DISCUSSION

Fuzzy Technique

This literature review is designed to review the use of fuzzy logic methods in student performance assessment. Previous research has noted the importance of applying
this fuzzy method, but few articles have examined how far fuzzy logic is applied to student performance assessment. As a result,
this review can be seen as one of the most
valuable studies in reviewing fuzzy methods
used to improve student performance assessment and also to assess the effects of this
method. In this review, we were able to identify and review 38 articles that applied different fuzzy logic techniques in student performance assessment from 2008 until 2018.
In this review, researchers consider the
classification suitable for the survey on the
fuzzy method. One of these classifications is

about classifying a number of articles published every year. In recent years, there has
been an increase in interest in applying fuzzy
methods in student performance assessment. It is shown that an increase in articles
published from 2.63% in 2008 reached
23.68% of all articles published in 2013. However, there was a significant decrease of articles published in 2014 and 2015, even in
2017 there were no article published about
fuzzy method in student performance assessment. But in 2016, articles that have been
published increased again and almost
reached 18.42%. Improvement of fuzzy techniques is possible because the results are
useful in improving student performance assessment, supporting teacher activity in the
classroom, and helping students develop independent learning processes (Baba et al.,
2012; Inyang and Joshua, 2013; HerreraViedma et al., 2009; Arguedas et al., 2018;
Chrysafiadi and Virvou, 2012).
The second classification is about the distribution of an articles number based on sample size. The sample size used in 38 reviewed
articles was very diverse. But we also found
that three out of the 38 articles did not explain explicitly the number of sample sizes
they used (Ingoley and Bakal, 2012; Owais,
2009; Do and Chen, 2013).

Fuzzy Set Theory (FST)
Fuzzy Rules Based (FRB)
Fuzzy Neural Network (FNN)
Fuzzy Information Retrieval (FIR)
Fuzzy Inference System (FIS)
Fuzzy Expert System (FES)
Fuzzy Decision Tree (FDT)
Fuzzy Decision Support System (FDSS)
Fuzzy Cognitive Map (FCM)
Fuzzy Classification (FC)
ANFIS

Positive
Negative

0

2

4

6

8

Total Effect of Using Fuzzy Logic

Figure 7. Articles distribution based on basic findings.

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Nia Amelia et al. Meta-analysis of Student Performance Assessment...| 84

The third classification is about the distribution of a number of articles based on context. We found 11 aspects used in this review. Based on these findings, it turns out
that the application of fuzzy logic is more
widely used in predicting student academic
performance. This is because the fuzzy logic
method is a practical method for evaluating
academic students and is very helpful in evaluating types of subjective answers (Ingoley
and Bakal, 2012; Yadav and Singh, 2011;
Oyelade et al., 2010; Yadav et al., 2014; Sakthivel et al., 2013; Yildiz et al., 2013; Arora
and Saini, 2013; Inyang and Joshua, 2013;
Rusli et al., 2008; Do and Chen, 2013; Hidayah et al., 2013; Borkar and Rajeswari,
2013).
The next classification is about the distribution of an article number based on data
types. Data types that are often used in the
application of fuzzy logic are more directed
towards comparative analysis methods,
where the study compares the use of fuzzy
logic with classic models (Daud et al., 2011;
Ingoley and Bakal, 2012; Barlybayev et al.,
2016; Gokmen et al., 2010; Yadav and Singh,
2011; Darwish, 2016; Oyelade et al., 2010;
Sakthivel et al., 2013; Bhatt and Bhatt, 2011;
Rusli et al., 2008; Ozdemir and Tekin, 2016;
Owais, 2009; Do and Chen, 2013). The fifth
classification is about the distribution of an
article number based on fuzzy techniques.
Based on the analysis of the researchers, 11
different fuzzy methods have been applied in
the reviewed articles. The FST, FRB, FIS, and
FES techniques have a high level among other
fuzzy techniques. The results show that the
level of use of this method is more applicable
in the assessment of student performance. It
is clear that fuzzy logic is the most widely
used method in science disciplines that have
high complexity and obscurity (Henriques et
al., 2018; Darwish, 2016; Sakthivel et al.,
2013; Zadeh, 1965).
The last classification is about the
distribution of articles based on basic

findings. Basic findings that were examined
from this literature review are whether or
not the fuzzy method used can have a
positive effect on student performance
assessment. Through the analysis, it was
found that around 97.37% of the 38 reviewed
articles believe that the use of the fuzzy logic
method could have a positive effect on
student performance assessment. The most
interesting finding is that among the fuzzy
logic methods applied, the eight studies used
the FST method, which has been able to show
the positive effects of fuzzy logic in student
performance assessment. This shows the
positive impact of FST as one of the most
common fuzzy logic methods used to
improve fuzzy logic and a reduction
ambiguity in predictions due to the
subjective assessment. In each of these
methods, 21.05% of the eight articles
applying this method have reported positive
effects on fuzzy logic (Daud et al., 2011;
Henriques et al., 2018; Baba et al., 2012;
Voskoglou, 2012; Vesely et al., 2016; Jamali
and Sayyadi Tooranloo, 2009; Voskoglou,
2013; Pöysä-Tarhonen et al., 2016), and
2.63% of articles considering the effects of
fuzzy method because the results obtained
do not make significant changes due to a
decrease in some student scores (Ozdemir
and Tekin, 2016).
6. CONCLUSION
This literature review states about previous research conducted on the use of fuzzy
logic methods on student performance assessment. In this case, several databases
were chosen to retrieve articles published in
the period of 2008 until 2018. To meet the
objectives of this study, all articles found
were classified by author, year of publication,
type of journal or conference, sample size,
context, data type, fuzzy technique, and basic
findings to improve student performance assessment. The results show that the level of
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articles published has been increased even
though there has been a decrease in several
years. Another goal in this review is to determine which fuzzy methods are most widely
used among researchers. The most obvious
finding emerging from this study is that applying fuzzy methods not only can improve
student performance assessment, but also
can support teacher activities in the classroom and help students to develop independent learning processes. Overall, when
more than 90% of studies reported positive
impacts using fuzzy methods to improve student performance assessment, the effectiveness of this method cannot be ruled out.

7. ACKNOWLEDGEMENTS
The authors would like to express their
thanks to the Program Studi Pendidikan
Teknologi dan Kejuruan, Sekolah Pascasarjana Universitas Pendididikan Indonesia, Jl.
Dr. Setiabudhi No. 229 Bandung, Indonesia.
8. AUTHORS’ NOTE
The authors declare that there is no conflict of interest regarding the publication of
this article. Authors confirmed that the data
and the paper are free of plagiarism.

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