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century, thanks to the work of Bloom, Gagne and
others. The goal of RLOs is having several pieces
of educational material to combine and reuse in
different contexts or for different purposes.
However, reusing learning objects has proven
more difficult than reusing software objects. It is
not clear how to recombine RLOs. While software
objects usually have a simple and clearly defined
interface, learning objects tend to have a complex
description (LTSC, 2002) and the interface for the
combination with other objects is not clear. Ad-
ditionally, there is an additional challenge: context
plays arguably an important role in learning, and
the de-contextualised nature of RLOs has been
argued to be their main drawback (Wiley, 2006).
The complex description of RLOs and their de-
pendency on the context hinder their reusability.
When it comes to thinking about lifelong
learning scenarios, these problems are extremely
relevant. Therefore, for the purposes of this chap-
ter, educational material will be thought to be any
useful educational resource that is available on
the web. This pragmatic view includes passive
resources (e.g. static web pages, documents,
spreadsheets), active resources (e.g. on-line ex-
ercises), and in general any other resource that is
suitable of being reused by a teacher or lecturer.
In other words, we will not restrict ourselves to
especially created learning objects that follow
some RLO paradigm (e.g. SCORM (Advanced
Distributed Learning, 2004)), due to the difficulties
and limitations of such an approach.
learning, it is reasonable to expect that adaptation
becomes more important as learners become more
diverse. In a lifelong learning situation, learners
interacting with a set of learning resources can be
expected to have a higher diversity of backgrounds
than the average classroom kids.
Thanks to the modern world wide web
(WWW), educational systems have the possibility
of offering an impressive amount of resources to
fit the needs of every learner. However, the pos-
sibility of becoming “lost in cyberspace” (Edwards
& Hardman, 1989) becomes an important risk.
Adapting educational material to the student is
key to prevent this from happening. Students tend
to remain focused when educational material is
adapted to their needs, thus creating a personalised
environment for each one (Cristea, 2004). There
is a compromise, however, between the need for
adaptation and the need for generality. It is dif-
ficult to personalise when the elearning system is
very general (e.g. freedom of exploration, large
knowledge domain, etc); on the other hand, when
the domain is reduced and/or well-known, it is
easier to get a high level of adaptation for good
learning results (e.g. flying simulators (Ludwig
et al., 2002)).
When it comes to adapting educational material
for learners, there are different sides of the learning
experience that can be adapted: content, learning
methods, temporal limitations, media selection,
content sequencing, help and hints selection, in-
formation hiding, etc. These and other aspects are
briefly discussed in (Paredes & Rodriguez, 2004;
Specht & Burgos, 2006) but, for the purposes of
this chapter, we will focus on one of them that
is very relevant for lifelong learners: sequencing
adaptation.
The Importance of Adaptation in LLL
Adapting the learning process to the individual
learners has a very positive impact on the learn-
ing. Bloom called this “the two-sigma problem”
(Bloom, 1984), quantifying that learners that
undergo a personalised learning experience get
results that are two standard deviations above
those of learners that get a one-size-fits-all ex-
perience (e.g. in a classroom). Although Bloom's
results were not obtained in the context of lifelong
Adaptation of Sequencing for LLL
Content sequencing has been an important topic
in the field of intelligent tutoring systems (ITS).
Some of the older ITS were able to modify the
order of some questions in a limited way (Barr
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