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et al., 1973). This has been referred sometimes
in the literature as task sequencing (McArthur
et al., 1988; Rios et al., 1993). Next step was
the sequencing of lessons, groups of learning
groups of a certain size, including learning con-
tent, questions, etc (Capell & Dannenberg, 1993;
Khuwaja et al., 1996). Moving one step forward,
some systems were designed to perform course
sequencing (Brusilovsky, 2000), being able to
reorder four types of tasks (presentation, example,
question, and problem) according to the current
knowledge that the system has about the students
and their goals.
Learning from all these experiences from the
ITS field, and taking into account the specific
needs of lifelong learning systems, there are four
goals that need to be undertaken when designing
a solution for adaptively sequencing learning
content: simplicity, scalability, reusability and
expressiveness.
First, the solution has to be as simple as possible
from the point of view of potential future users
(i.e. learning designers). Simple solutions tend to
work better than complex ones for a number of
situations, and only simple tools that are easy to
use are accepted in the long run by practitioners
and authors. Several AI techniques have been
used in the past to adapt content sequencing, in-
cluding planning (Ullrich, 2005), ontology-based
reasoning (Karampiperis & Sampson, 2004),
and combination of semantic web techniques
with SCORM (Baldoni et al., 2004), but they
require a high technological background to be
understood. Some authors have proposed to use
UML as a language to describe adaptive sequenc-
ings of learning material (Dolog & Nejdl, 2003;
Papasalouros et al., 2004); however, UML is a
language well-known among computer engineers,
but can be more difficult to use by other learning
designers. Our solution is based on graphs, and
is thus similar to the approaches of systems like
AHA! (Bra et al., 2003), although AHA! focuses
on content and presentation adaptation rather than
sequencing.
Second, the solution proposed should be scal-
able. This means that it should be able to handle
a large number of activities to be sequenced.
The point here is not on technical scalability
or technical performance (although that is also
important). Instructional designers working for
lifelong learners must be able to define adaptive
sequencings of learning content that sometimes
involve a huge number of learning activities,
and this must not make their task unmanageable.
The approach proposed here is hierarchical, un-
like other graph based approaches like AHA! or
Paraschool [Semet et al., 2003].
The third goal is reusability. This has two di-
mensions. First, it must be possible to easily change
one activity for another equivalent one (e.g. same
learning goals, same parameters, etc) without a
change in the overall sequence. Second, the effort
on reusability has a second lecture of integration
and combination. If a sequencing has been already
defined for a set of activities, it should be easy
to get everything (activities plus sequencing) as
a black box and integrate it as part of a broader
picture, with little or no effort. This is related to
the former point about scalability. These goals are
usually taken into account in IMS specifications
like IMS Simple Sequencing (IMS, 2003d), IMS
Learning Design (IMS, 2003a), or IMS Common
Cartridge (IMS, 2008).
Last, but not least, a sequencing definition
mechanism has to make it be easy to define cycles.
This is an extremely important issue in learning,
and the failure to address this fact is a big lack
on the part of many elearning systems. Reflection
is crucial for long-term understanding (Marek &
Cavallo, 1997); and a sequence of learning activi-
ties that takes into account forget-remember cycles
will lead to more productive learning for life.
SEQUENCING GRAPHS
This chapter concentrates on adapting the sequence
of learning resources that a student interacts with.
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