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where the context-aware profiles permit mobile
users to state their personal preferences for par-
ticular situations when using web-based systems.
The preferences vary based on the current context.
Then a filtering process is applied to the user's
current context and the user's preferences for this
context. The process selects the context-aware
profiles that match the user's current context, and
then it filters the available informational content
based on the selected profiles.
Barkhuus and Dey (2003) argue that context-
aware applications are preferred over personal-
ized ones, where personalization in the sense of
adaptability is used. Thus, the application allows
the user to specify their settings for how the ap-
plication should behave in a given situation.
The learning environment described by Yang
(2006) consists of three systems: (1) peer-to-peer
content access and adaptation system; (2) person-
alized annotation management system; and (3)
multimedia real-time group discussion system.
It uses the ubiquitous learning paradigm, with
features such as identifying the right collabora-
tors, contents and services in the right place at
the right time, based on a learner's surrounding
context such as where and when the learners are
(time and space), what the learning resources and
services available for the learners are, and who the
learning collaborators are that match the learners'
needs (Yang, 2006). Our approach does not rely
on the context of the learner, but it uses user pro-
files to provide recommended learning contents
and recommended users. On the other hand, the
context aware ubiquitous learning environment
has neither recommended learning materials nor
recommended collaborators.
The system described by Perscha et al. (2004)
provides context information in a presentation-
independent format that can be used for mobile
learning teams for synchronous and asynchronous
communication means. In our approach, we are
currently adding a communication facility which
can be used by the recommended (expert) learners
to help other learners by answering their questions.
LearnWeb 2.0 (Marenzi et al. , 2008) is a
platform for sharing and discussing as well as
creating knowledge resources, which allows for
integration of social networks, such as Facebook
and Flickr. The integrated infrastructure in Learn-
Web 2.0 relies on external Web 2.0 applications.
Therefore, one of the platform's main challenges
is determining which Web 2.0 tool should be used,
as not all Web 2.0 applications are open source,
and not all of them actually provide APIs to con-
nect to LearnWeb 2.0. In MOT 2.0, we use the
concepts of Web 2.0 (rating, tagging, feedback)
applied within the system, and not by integrating
external ones.
Calvani et al. (2008) argue that each model
of lifelong learning should take into account the
following main factors.
1. The complexity and the variety of the types
of knowledge involved. In MOT 2.0, we
have covered the variety of the types of
knowledge, as the privileges in each group
are based on the knowledge level (i.e., the
higher the knowledge level the more privi-
leges the user has).
2. The dimension of self-directed learning. This
dimension is also covered in MOT 2.0, as
the system can track all actions of the user,
and uses these actions in the recommendation
process (i.e., recommend learning content
rated 4/5 or higher, recommend users who
are experts).
3. The dimension of informal learning. In MOT
2.0, both formal and informal learning are
supported. In the case of informal learning,
the Web 2.0 features in MOT 2.0 facilitate
the learning process, during the work, the
study, or any other activities.
4. Multiple dimensions of the technological
solutions. This factor is a challenging one —
currently MOT 2.0 is a Web 2.0 application,
which can be integrated with any LMS, or
any other web applications that support Java
and Tomcat.
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