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addressing in the context of the LLL
paradigm. For instance, how to sup-
port intuitiveness for a particular type
of student based on learning personal
records. Solving these type of prob-
lems may produce a major impact
on learners with functional diversity
issues, such as prelocutive deafness,
since they have difficulties achiev-
ing functional levels of the oral and
written language, so emotional com-
munication online is deficient (Garay
et al., 2006), and some vocabulary
adaptations could be of interest.
and motivation. The latter can be ad-
dressed trough scrutability features so
that users are provided with features
to see and appreciate the meaning of
personal information a user model.
It is expected that helping people to
become more self-aware and avoid
self-deception, because their user
model mirrors their real actions, will
encourage meta-cognition and deeper
learning (Kay, 2006).
•
Flexibility and ubiquitous scenarios: the
LLL paradigm, where students are sup-
posed to follow formal and informal learn-
ing from any context, demonstrates the
need for developing standard user and de-
vice profiles that can respond not only to
current everyday scenarios, but also can be
flexible enough to cope with future scenar-
ios coming from ubiquitous and wearable
computing. In addition to these require-
ments, context/location-awareness, seam-
less roaming, and portability have also
been identified by some authors (Velasco
et al., 2004). In this context, there is a need
to extend these previous efforts into a stan-
dardized framework that will facilitate in-
teroperability and merging of device and
user profiles. Privacy and trust problems
shall be considered as well (Wiedenback et
al., 2003).
◦
Intelligent monitoring process to
adapt to learners expectations and le-
verage the exploitation of the learn-
ing process. Current user tracking
approaches are to be extended to
monitor the progress of users, with
the aim of providing sensible reports
to learning designers and course au-
thors, who can then make improve-
ments to the initial design. Auditing
facilities will include support for
identifying, defining, and evaluating
any type of object or learning situa-
tion that needs to be audited.
◦
Evidences of learning better: one of
the most critical issues to guide the
learning process via adaptive fea-
tures is the management of the effec-
tiveness of the learning process and
its evolving features. In this regard
objectives are twofold: first, the ap-
proach will support the identifica-
tion, labeling and managing of learn-
ing measurements so that teachers,
designers, tutors and administrators
will benefit from specific features
that impact on learning effectiveness.
Second, learners will be provided
with evidences of their learning pro-
cess to encourage their engagement
•
Long-term modelling and profiling: A user
profile/model is an explicit representation
of the properties of an individual user and
can be used to reason about the needs, pref-
erences or future behaviour of that user.
These data includes information for the
user about cognitive models, pedagogical-
style models, educational goals and moti-
vation model, domain expert knowledge
model, background model, bugs and mis-
understandings model and collaborative
learning model. Moreover, other types of
user data can be obtained, such as instruc-
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