Fuzzy Logic in Student Modeling - Advances in Personalized Web-Based Education - page 60

Table 2.11 Correlation of an e-shop and an adaptive e-learning system concerning the presented
rule-based fuzzy log ic system
E-shop E-learning

Nodes Products Domain concepts

Preferences' dependencies Knowledge dependencies

Arcs

Descriptions of a preference (e.g.
'uninterested', 'interested', 'liked',
'preferred')

Descriptions of knowledge level (e.g.
'unknown', 'insufficiently known',
'known', 'learned')

Fuzzy sets

Preferences Knowledge level

Changeable
states

or assimilating. The presented rule-based fuzzy logic system identifies and updates
each time the student's knowledge level not only for the current concept, which is
delivered to the learner, but also for all the related concepts with this concept. To
achieve that, the system considers either the learner's performance or the knowledge
dependencies that exist between the domain concepts of the learning material. In
the particular rule-based fuzzy logic system, fuzzy sets are used in order to describe
how well each individual domain concept is known and learned. Furthermore, it
uses a mechanism of rules over the fuzzy sets, which is triggered after any change
of the value of the knowledge level of a domain concept and updates the values of
the knowledge level of all the related domain concepts with that. Therefore, the edu-
cational system, which has integrated the particular rule-based fuzzy logic system,
is able to makes dynamic decisions on how the teaching syllabus is presented to the
learner to fit his/her personal needs and learning pace.
The operation of the system is based on the knowledge domain representation that
is implemented through a Fuzzy Related-Cognitive Network. This kind of knowl-
edge domain representation helps to manage to represent either the order in which
the domain concepts of the learning material have to be taught and organized, or the
knowledge dependencies that exist between the domain concepts. This is signifi-
cant because the knowledge level of a domain concept increases or decreases due to
changes on the knowledge level of a related domain concept. The design of the learn-
ing material and the definition of the individual domain concepts that it includes, are
based on the knowledge and experience of domain experts. Furthermore, the contri-
bution of domain experts is significant for the definition of the knowledge dependen-
cies that exist among the domain concepts of the learning material and their “strength
o impact” on each other.
The presented rule-based fuzzy logic system is applicable to systems, in which the
user's changeable state and/or preferences are affected by the existing dependencies
among the system's elements (like concepts, preferences, events, choices). Thereafter,
the particular system could be implemented in adaptive systems other than adaptive
tutoring system. For example, it could be used in an e-shop, where the preference of
an online shopper for particular products can be used in order to guess and propose
her/him other products that the user is likely to be interested in. In the Table 2.11 the
correlation of an e-shop and an adaptive e-learning system is presented concerning
the particular rule-based fuzzy logic system (Table 2.11 ).