Information Technology Reference
In-Depth Information
Goodkovsky (2006) extends Goodkovsky
(2004) in several aspects. It separates the logic
and media of tutoring completely in order to gen-
eralize and reuse logic with any media. The suite
of tutoring tasks is extended/simplified. Learning
resources are represented uniformly enabling
unification/simplification of their processing.
Another trend involves CL. IESs generally
do not focus on CL although there is progress in
the field of Intelligent Collaborative Supported
Learning (Devedzic, 2005). Combinations of
LMS and IES technologies will advance towards
this direction.
Another key technology involves mobile
learning (m-learning), an e-learning paradigm
exploiting developments in wireless infrastructure
to provide ubiquitous access to knowledge with
mobile devices (Glavinic, Rosic & Zelic, 2008).
Mobile learning will play an important role in
lifelong learning (Holzinger, Nischelwitzer, &
Meisenberger, 2005) due to the fact that most of
the people possess and very frequently use mo-
bile devices. Furthermore, it enables learning in
almost every location and in different contexts.
Most of existing IESs have not been designed for
m-learning. Certain issues need to be addressed
when developing mobile IESs (Glavinic, Rosic
& Zelic, 2008).
It is generally believed that complex problems
are easier to solve with hybrid approaches. How-
ever, use of hybrid KR&R techniques in IESs has
not become yet a popular trend. It is expected that
in the following years various IESs employing
hybrid KR&R techniques will be developed.
It should be also mentioned that AI methods
can be used in recommender systems for life-
long learning. Recommender systems search
for potential learning activities and recommend
the most suitable ones to lifelong learners. CBR
has been employed in personal recommender
systems (Drachsler, Hummel, & Koper, 2008).
Furthermore, agent-based technology can also be
used. For instance, in (Santos, 2008) a multi-agent
approach to a recommender system for lifelong
learning is presented. It is possible that a hybrid
KR&R technique could offer benefits to recom-
mender systems for lifelong learning.
Finally, Semantic Web-based intelligent edu-
cational systems (SWBIESs) is a new category of
educational systems (Simic, Gasevic & Devedzic,
2006). Information (e.g. learning resources) in the
FUTURE RESEARCH DIRECTIONS
A key trend for IESs involves efficient creation/
management/searching of reusable learning ob-
jects residing in learning content repositories. Such
a trend will aid development of WBIESs, combi-
nations of WBIESs with conventional e-learning
systems and communication among WBIESs.
Moreover, advanced features and generations of
learning objects specifically addressed to IESs may
appear such as learning knowledge objects, which
are knowledge-based, theory-aware, dynamically
generated and provide a tutoring service interface
(Zouaq, Nkambou & Frasson, 2008).
Combinations of LMS and IES technologies
have recently started to be developed. Results
seem to be promising. It is very likely that such
combinations will become a key trend due to
increasing popularity of LMSs and WBIESs as
well as existence of numerous learning content
repositories. Such a development will be beneficial
to lifelong learners as they will have the chance
to pursue learning using systems offering various
ways of communication/collaboration, content de-
livery and learning activity management (Tomei,
2009; Inoue 2009).
Agents constitute a key technology for IESs.
It is generally admitted that agent-based technol-
ogy is important in the context of the Web (e.g.
for implementing Web services), but, although it
is widely used in other application domains, like
e-commerce, its use in web-based education has
not become yet a popular trend. Agent-based tech-
nology can play a significant role in combination
with most of the trends mentioned in this section.
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