Conclusions and Discussion - Advances in Personalized Web-Based Education - page 116

Contribution to Knowledge Domain Representation
The operation of the system is based on the knowledge domain representation
that is implemented through a Fuzzy Related-Concepts Network (FR-CN). This
kind of knowledge 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 among the domain concepts.
This is significant 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 learning material and the definition of the individual domain
concepts that it includes, are based on the knowledge and experience of domain
experts. Furthermore, the contribution of domain experts is significant for the defi-
nition of the knowledge dependencies that exist among the domain concepts of the
learning material and their “strength o impact” on each other.
The particular knowledge domain representation approach helps the system to
recognize either the domain concepts that are already partly or completely known for
a learner or the domain concepts that s/he has forgot, taking into account the learner's
knowledge level of the related concepts of the learning material. As a consequence,
the presented knowledge domain representation approach contributes to the improve-
ment of the navigation support that an adaptive and/or personalized learning system
provides. Furthermore, the presented approach represents the knowledge domain in
a more realistic way. It constitutes a prototype for an adaptive and/or personalized
tutoring system for delivering the learning material to each individual learner dynamically,
taking into account her/his learning needs and different learning pace.
Contribution to Student Modeling
The target of this topic was to show how fuzzy sets can be combined with other
student modeling techniques to promote adaptivity and personalization in educa-
tional applications. The evaluation of the novel approach, which was presented
in this topic, revealed that the incorporation of fuzzy techniques into the student
model contributes significantly to the adaptation of the learning process to the learning
pace of each individual learner. The presented novel fuzzy student modeling approach
allows the system to identify the appropriate domain concepts that correspond to each
individual learner's knowledge level and educational needs. Therefore, it improves
the efficiency of the adaptivity of the instructional process.
The presented fuzzy student modeling approach models automatically the
learning or forgetting process of a student. In particular, it helps the learners that
already know some concepts of the teaching material to save time and effort dur-
ing the learning process. Furthermore, the presented approach helps the system
to recognize which domain concepts of the learning material that the student has
already learned in previous interactions, s/he has forgotten and adapts the presen-
tation of material accordingly.