Adaptive Sequencing of Information for Lifelong Learners - E-Infrastructures and Technologies for Lifelong Learning - page 289

on an activity, and status of the objectives of an
activity. IMS Simple Sequencing lacks a student
model in which to store part of the information
that the system is able to track about him/her,
apart from these three aspects. Thus, it is not
possible to fully express all sequencings that can
be defined by a SG in terms of this specification.
For example, the sequencing cannot be influenced
according to a “skill level” of the student, because
there is no such concept in the specification and
there are no means to include it.

in a UoL; although the mechanism is not trivial.
The process of expressing Sequencing Graphs
in IMS LD semantics has shown several limita-
tions of the specification when it is used to define
adaptive sequencings. High-level authoring tools
are thus necessary to implement such complex
reorganizations of content in IMS-LD, and Se-
quencing Graphs can provide that functionality
by presenting a simple metaphor that hides part of
the complexity of the specification (c.f. RELOAD
Learning Design editor).
One of the main limitations of IMS LD is the
difficulty of introducing adaptation in the UoL
during runtime. The method showed in the chapter
makes use of global elements, but there are other
two interesting proposals. The first one is the in-
tegration of active components into the IMD-LD
player, as proposed in (de la Fuente et al., 2009).
The authors propose the integration of the player
with active external components. They prove their
point using Google Docs spreadsheets, but claim
their architecture is general enough to integrate
other plug-ins and services. Another possibility
is the design of an architecture that supports ac-
cess to information on runtime (Zarraonandia et
al., 2006). The goal is to enhance reusability of
the UoL introducing simple modifications into
the original learning process.

CONCLUSION AND FUTURE WORK

Sequencing Graphs have been designed to define
a set of adaptive sequencings given some learning
activities. Sequencing graphs are a specialization
of finite automata that take into account many
of the particular aspects of the learning process,
and have been used before in several elearning
systems (Gutierrez et al., 2004; Prieto-Linillos et
al., 2006). Additionally, they try to fulfill four de-
sign goals: simplicity, expressiveness, scalability,
and reusability. They have been designed to be
simpler to create than other alternatives, yet able
to define any type of sequencing, in particular
those involving cycles. They are hierarchical,
which helps to manage big numbers of learning
activities and makes the approach scalable. The
other main goal they have been designed for is
reuse of sequencings, from a double point of
view: the upgrade of an activity does not affect
the sequencing of the set; and a set of activities
with their sequencing defined as a graph is suit-
able of being integrated in a broader set, that is,
to be included as a container node in a graph of
a higher level of hierarchy.
A sequencing graph can be expressed in terms
of IMS LD, thus providing interoperability be-
tween IMS LD compliant systems and systems
based on sequencing graphs. Although the specifi-
cation is not exactly designed for sequencing, the
mechanisms provided (i.e. properties, conditions
and actions) permit to articulate the same strategy

REFERENCES

Advanced Distributed Learning. (2004). SCORM
2004: Sharable Content Object Reference Model .
ADL.

Albert, J., Frank, S., Hafner, U., & Unger, M.
(1997). Video compression with weighted finite
automata . In Data Compression Conference.

Baldoni, M., Baroglio, C., Patti, V., & Torasso, L.
(2004). Reasoning about learning object metadata
for adapting SCORM courseware. In Proceedings
of the International Workshop on Engineering the
Adaptive Web , (pp. 4-13).