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(.) (.) (.) (.) (.) (.) (.) (.) (.)
After, we will gather the text sources (if available) and full-index them (index terms generation), in
order to provide data to refined searching and topic context definition. If due to some circumstances
we cannot do full indexing, we substitute it as much as possible with a list of searchable terms
collected in reviews or web pages where the text could be referred or commented. With the reference
list data and the generated indexes we build a concept map (suggestion knowledge acquisition),
representing the knowledge (Figure 2) that will support the suggestion work. With the execution of
these three initials tasks execution we finish the first stage of the readings suggestion life cycle – the
data preparation.
The second stage – studying session evaluation – is concerned exclusively in following what students
request to the agent, and what kid of suggestion that accept and preferred. During this process, the
agent stores all the requests done – studying session profile -, per student and per studying session,
and the readings selection selected. Finishing a studying session, the agent evaluates the preference
level for each reference that was selected – student preferences evaluation -, and rewrites its own
knowledge base, updating the student profile, and registering its preferences. As we can predict,
process after process, the agent’s knowledge base will be richer, and will provide better readings
suggestions and cross-referring paths. This second task group of the agent’s life cycle will be
executed so many times as the number of studying sessions performed under its assistance. While
the first one is executed only when the lecturing team decide to restructured the course’s programme
and the list of bibliographic references.
Figure 3: Agent’s conceptual architecture components
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