Techniques, Technologies and Patents Related to Intelligent Educational Systems - E-Infrastructures and Technologies for Lifelong Learning - page 4

sion and employability. Furthermore, in modern
society, people and organizations undergo pro-
cesses of transition. Each one must be prepared
for transitions, engaging in lifelong learning as a
fundamental strategy for handling change (Field,
Gallacher, & Ingram, 2009). A main driving force
of lifelong learning is e-learning. Numerous
computer-based systems have been developed for
education during the last decades. Such systems are
usually addressed to school students, pre-graduate
and post-graduate university students, employees
of organizations/companies, unemployed and
generally members of online communities.
An important requirement when e-learning
systems are used for lifelong learning is the
capability to personalize instruction to the needs
and skills of learners. This requirement becomes
increasingly vital as the number of learners ac-
cessing an e-learning system increases. Learners
have different preferences and learning styles,
set diverse educational goals and usually have
unequal knowledge levels regarding a specific
teaching subject. Lack of sufficient spare time due
to pressing (family/professional) obligations is a
factor frequently resulting in loss of interest when
interacting with ineffective e-learning systems.
An e-learning system tailored to learner needs/
skills saves learners time/effort and motivates
participation in learning process. Traditional
Computer-Assisted Instruction (CAI) systems
are based on shallow representation of teaching
domain, learner data and pedagogical methods.
It is difficult for them to adjust effectively the
learning process as they provide limited ways of
adaptation and learner evaluation.
These drawbacks gave rise to a new generation
of educational systems encompassing intelligence
called Intelligent Educational Systems (IESs)
(Aroyo, Graesser & Johnson, 2007; Brusilovsky
& Peylo, 2003). IESs incorporate Artificial
Intelligence (AI) techniques/mechanisms to
model learners as well as knowledge regarding the
teaching subject and tailor learning experience to
learner needs. Main IES types are Intelligent Tu-

toring Systems (ITSs) and Adaptive Educational
Hypermedia Systems (AEHSs) using intelligent
methods. IESs support lifelong learning as they
provide personalized instruction. IESs place
lifelong learners at centre stage by making them
more responsible for the results of the learning
process (Drachsler, Hummel, & Koper, 2008).
Due to the fact that IESs can be used effectively
in different contexts, they satisfy lifelong learning
requirements. IES functionality can be used in
education institutes but can be also integrated into
workplace learning and personal development.
An interesting aspect of IESs is their constant
evolution by exploiting advances in Web-based
technologies, AI techniques and Computer Science
in general. Advances in these fields provide the
impetus to develop advanced IES applications that
satisfy learning necessities not covered by previous
systems. Various AI methods have been applied to
IESs, enabling implementation of several online/
offline intelligent functions to accommodate dif-
ferent IES user types. Use of several AI methods
in IESs has been vastly explored. However, use of
certain other AI methods has not been adequately
explored.
Figure 1 depicts an IES's basic architecture.
It mainly consists of the following components:
(a) domain knowledge , which contains learning
content and information about the learning subject,
(b) user (or student) modeling unit , which records
learner information, (c) pedagogical module ,
which encompasses knowledge regarding various
pedagogical decisions, (d) user interface , which
communicates with users.
Sometimes, an extra component is considered,
namely expert module . Expert module typically
deals with interactive problem solving, e.g. with
providing intelligent help. It acts as an expert
(tutor) who supervises learners as they solve
problems and gives advice, hints, examples etc.
This module can be considered as part of peda-
gogical module in Figure 1.
In this chapter, we outline technologies and
techniques used in ITSs and AEHSs, in all their