Fuzzy Logic in Student Modeling - Advances in Personalized Web-Based Education - page 32

environment that aimed to help students to construct the concepts of vectors in
physics and mathematics. The particular fuzzy-based student model allows the
diagnostic model to some extent imitate teachers in diagnostic students' charac-
teristics, and equips the intelligent learning environment with reasoning capa-
bilities that can be further used to drive pedagogical decisions depending on the
student learning style. Moreover, Jia et al. (2010) have applied fuzzy set theory
to the design of an adaptive learning system in order to help learners to memory
the content and improve their comprehension. Also, Goel et al. (2012) have used a
fuzzy student model for facilitating the student reasoning process, which is based
on imprecise information coming from the student-computer interaction, and pre-
dicting the degree of error that a student is possible to make in the next attempt
to a problem. In addition, Salim and Haron (2006) have provided a personalized
learning environment that exploit pedagogical model and fuzzy logic techniques.
Other educational systems that have incorporated fuzzy logic techniques into the
student model are: F-CBR-DHTS (Tsaganoua et al. 2003); TADV (Kosba et al.
2003, 2005) and DEPTHS (Jeremi´ et al. 2012).
2.3 Fuzzy Logic for Knowledge Representation
The knowledge domain module is one of the most major modules of an Intelligent
Tutoring System (ITS). The knowledge domain representation is the base for the
representation of the learner's knowledge, which is usually performed as a subset
of the knowledge domain. It contains a description of the knowledge or behaviors
that represent expertise in the subject-matter domain the ITS is teaching. In other
words, the knowledge domain module is responsible for the representation of the
subject matter taking into account the course modules, which involve domain
concepts. The particular module has been introduced in ITS but its use has been
extended to most current educational software applications that aim to be adaptive
and/or personalized.
To enable communication between system and learner at content level, the
domain model of the system has to be adequate with respect to inferences and
relations of domain entities with the mental domain of a human expert (Peylo
et al. 2000). Therefore, the knowledge domain representation in an adaptive and/
or personalized tutoring system is an important factor for providing adaptivity. The
appropriate approach for knowledge representation makes easier the selection of
the appropriate educational material satisfying the student's learning needs. The
most common used techniques of knowledge domain representation in adaptive
tutoring systems are hierarchies and networks of concepts.
A hierarchical knowledge representation is usually used in order to specify
the order in which the domain concepts of the learning material have to be taught
(Chen and Shen 2011; Siddara and Manjunath 2007; Vasandani and Govindury
1995), and can be implemented through trees (Kumar 2005; Geng et al. 2011).
For example, in INMA, which is a knowledge-based authoring tool for music