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2.2.4 Applications of Fuzzy Logic
The ability of fuzzy logic to handle the uncertainty, imprecise and incomplete
data, and information that is characterized by human subjectivity makes it useful
in many human-centric fields. Mendel (2007) has categorizes the applications of
fuzzy logic in: approximation; clustering; control; databases; decision making;
embedded agents; health care; hidden Markov models; neural networks; noise
cancellation; pattern classification; quality control; spatial query; wireless com-
munications. In addition, fuzzy set theory has been applied in education and edu-
cational systems. The applications of fuzzy logic in the educational field can be
categorized into:
Grading systems : Fuzzy logic is used to define the grade (as a letter, as a num-
ber, or as a percentage) that characterized the student's level of achievement.
Examples of fuzzy applications in grading systems are the researches of (Bai
and Chen 2006a, 2006b, 2008; Biswas 1995; Cheng and Yang 1998; Echauz and
Vachtsevanos 1995; Law 1996; Wang and Chen 2006; Wilson et al. 1998).
Student's evaluation : It includes an overall assessment of the student's learn-
ing. In particular, it is a complex process that includes student's performance,
abilities, skills and learning characteristics. Some of the fuzzy logic applications
in the process of the student's evaluation, which appear in the literature, are
the following: (Chang and Sun 1993; Chen and Lee 1999; Ma and Zhou 2000;
Nyk¦nen 2006; Weon and Kim 2001).
Learning adaptation : Learning and teaching are complex processes that have
to consider each individual student's characteristics and abilities in order to be
effective. The educational systems have to adapt dynamically to each individual
learner's needs and abilities. Many researchers (Alves et al. 2008; Jili et al. 2009;
Jurado et al. 2008; Kosba et al. 2003; Suarez-Cansino and Hernandez-Gomez
2008) have used fuzzy logic for providing learning and teaching adaptation.
2.2.4.1 Applications of Fuzzy Logic in Student Modeling
The aim of the adaptive and/or personalized tutoring systems is to readjust each
time the instructional process and the teaching strategy considering the student's
needs and abilities. This operation is based on human subjectivity and concep-
tualizations. That is the reason for the need of fuzzy logic. Therefore, there are
many researchers that have used fuzzy logic techniques in student modeling to
deal with uncertainty in the student's diagnose. For example, Xu et al. (2002) have
used fuzzy models to represent a student profile in order to provide personalized
learning materials, quiz and advices to each student. Furthermore, KavLJiLJ (2004a)
have succeeded to provide personalization of navigation in the educational con-
tent of InterMediActor system through the construction of a navigation graph and
the adoption of fuzzy logic into student reasoning. A fuzzy-based student model
has been applied, also, by Stathacopoulou et al. (2005) to a discovery-learning
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