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ing context (Santos and Boticario, 2010a). This
modelling complements traditional approaches
of recommender systems that apply blindly the
algorithms to data gathered from interactions.
These recommendations can be managed through a
recommendations model that is built up following
the appropriate standards. Details on the model
are described in (Santos & Boticario, 2010b). This
formal model allows creating recommendations
at design time in terms of applicability conditions
and restrictions, which are later used at runtime
for selecting the appropriate recommendations
for the user in the context at hand. At this point,
algorithms can be used to adjust existing recom-
mendations or produce new ones. The model
covers the following needs of information (see
Tables 1 and 2): what is recommended, when is
recommended, how is recommended and why is
recommended.
This process covers the lifecycle of service
provision since the different phases are properly
managed. First, a methodology is used (Santos et
al., 2009) to support the course designer in de-
scribing recommendations in learning inclusive
scenarios (design phase). Second, a management
tool is required to allow at publication time the
readjustment of the designed recommendations
as well as the validation of the automatically
produced ones (Santos et al., 2011). Third, the
recommendations that match the user needs and
context are offered to the user and present addi-
tional information to the user to explain why the
recommendation has been offered (Santos and
Boticario, 2008b). Finally, the system requests
explicit feedback from the learner when she has
shown interest in the recommendation process to
improve the recommender and provides reports
on the recommendations tracking.
Following the methodology proposed above,
several recommendations were identified to offer
the dynamic guidance embedded in the adaptive
and inclusive psycho-educational support ser-
vice. The scenario requires users to go through
questions that reveal their psycho-educational
background in order to build on skills and abili-
ties, such as learning style, competencies already
had, educational background, experience of using
computers and so on. After fulfilling the question-
naire, learners are invited to take a course that
will introduce them to the university's virtual
learning environment, as well as to support e-
services available at the institution. The course
Table 1. Example of a recommendation with additional material
Recommendation 1: Depending on the marks obtained in questionnaire, additional material is provided to the learner
What is recommended
A learning object of the course
When is recommended
When the learner has submitted the responses to a questionnaire
How is recommended
A link to the learning object selected is provided
Why is recommended
The recommendation is provided by taking into account the previous knowledge of the learner
Table 2. Example of a recommendation to foster collaboration
Recommendation 2: Foster collaboration by using the forums
What is recommended
The forums service in the platform
When is recommended
When the learner has not communicated with her peers
How is recommended
A link to the forum tool
Why is recommended
The recommendation is provided by taking into account the collaboration level of the users in order
to promote collaboration among peers in the course
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