Fuzzy Logic in Student Modeling - Advances in Personalized Web-Based Education - page 25

Chapter 2
Fuzzy Logic in Student Modeling

Abstract The significant development of the e-learning systems has changed the
ways of teaching and learning. In nowadays, everyone can have access to e-learning
systems from everywhere. Therefore, the e-learning systems have to adapt the
learning material and processes to the needs of each individual learner. However,
learning and student's diagnosis are complex processes, which deal with uncertainty.
A solution to this is the use of fuzzy logic, which is able to deal with uncertainty
and inaccurate data. This chapter explains how fuzzy logic can be used to automati-
cally model the learning or forgetting process of a student, offering adaptation and
increasing the learning effectiveness in Intelligent Tutoring Systems. In particular,
it presents a novel rule-based fuzzy logic system, which models the cognitive state
transitions of learners, such as forgetting, learning or assimilating. The operation of
the presented approach is based on a Fuzzy Network of Related-Concepts (FNR-C),
which is a combination of a network of concepts and fuzzy logic. It is used to rep-
resent so the organization and structure of the learning material as the knowledge
dependencies that exist between the domain concepts of the learning material.

2.1 Introduction

Over the past decade, the rapid development of computer and Internet technolo-
gies has affect a variety of fields of the human's everyday life. Such a field is the
education. The ways of teaching and learning have been changed and the e-learn-
ing systems and processes have been developed significantly. E-learning systems
offer easy access to knowledge domains and learning processes from everywhere
for everybody at any time. As a result, users of web-based educational systems are
of varying backgrounds, abilities and needs. Therefore, the e-learning systems and
applications have to offer dynamic adaptation to each individual student.
Adaptation is performed through the student model. In particular, the student
model is a core component in any intelligent or adaptive tutoring system that is
responsible for identifying and reasoning the student's knowledge level, miscon-
ceptions, abilities, preferences and needs. The student model represents many