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they can reach their learning goals if (and if
working on a common group goal, only if)
the other students in the learning group also
reach their goals (Deutsch, 1962; Johnson &
Johnson, 1989). Whilst there is no guarantee
in general that students would recognize
this fact, by visualizing the common goal
this recognition could be brought forward
by the system. Groups also can be adapted
to, as will be shown later.
the key elements for extending learning activities
and making the learning process more effective.
Personalization, customization and adaptation
to the user, are terms frequently used in the areas
of user modeling (Rich, 1979) and adaptive hyper-
media (Brusilovsky, 1996), and refer to showing
each user the exact information they need, when
they need it, and where they need it.
Adaptivity and personalization can be applied
to content, in the sense of delivering appropriate
information to the user. More importantly for
Web 2.0 applications, unlike adaptation in regular
personalized e-learning systems where adaptation
is focused on the individual, adaptation can take
into account the different interacting users of a
system. This means that adaptation can be deliv-
ered based on user groups. This can take the form
of showing similar content to users with similar
interests. Also unlike classical personalization,
adaptation can also take the form of bringing us-
ers with similar interests together, and allowing
them to communicate directly with each other.
In educational applications, these users are the
learners. Finally, adaptation can also be applied
to recommend experts or teachers to learners, or
point out to teachers which students are in need
of help.
In this chapter, we therefore approach the life-
long learning paradigm from the point of view of
merging research on personalization and adaptive
e-learning with Web 2.0 technologies .
As the whole topic is dedicated to lifelong
learning, we will not attempt to define this para-
digm, leaving this to chapters elsewhere. Instead,
we tackle the two other topics - adaptation and
Web 2.0 - and finally, using a concrete case study,
we illustrate how the merge can be achieved.
To better understand the theoretical framework
underlying such a merge, we begin by making
a comparative analysis of previous models and
frameworks for adaptive, personalized systems.
This analysis allows us later to explain how a social
reference framework for adaptive e-learning can
be built, both from a theoretical as well as from
The successor to Web 2.0 is Web 3.0 (Metz,
2007), where the semantic search and browsing
are made possible by natural language processing
and Semantic Web technologies (Social Semantic
Web), and Web 4.0 and beyond (Metz, 2007) are
already being discussed. Clearly, these new tech-
nologies attract both developers and users alike,
and, as lifelong learners are to be found in both
categories, lifelong learning providers have to
expect a discerning public that expects teaching
to use the latest technologies.
Thus, lifelong learning and Web 2.0 comple-
ment each other: where lifelong learning is about
learning anywhere, anytime, Web 2.0 allows for
collaboration during learning, as well as during
the creation of the learning content. Addition-
ally, both lifelong learning and Web 2.0 rely on
the users (learners) more than the content itself,
where the users (learners) determine their own
learning pace (in lifelong learning), or create,
evaluate (rate) and edit the content (in Web 2.0).
E-learning 2.0 thus emerges from the combination
of 'regular' e-learning and Web 2.0 technologies,
and in the case of lifelong learning this leads to
Lifelong Learning 2.0.
However, with the massive amount of (general)
information available through Web 2.0, it is becom-
ing harder for learners to learn, or even to find,
related communities, peers, and content, and this
makes the process of lifelong learning using Web
2.0 less efficient. To overcome this challenge, we
perceive adaptive and personalized techniques as
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