From the client side, we have utilized smart devices with content adaptation as shown in Figure 3 and sensor
networks to sense and react to the learners’ surrounding environment. Almost any information available at the
time of an interaction between learners and Web systems can be seen as contextual information such as location
and temporal information, knowing where is the learner and what he/she is doing during a certain period of time,
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whom you are working with, and people that are nearby, resources that are nearby (e.g. accessible devices, and
hosts), etc. We are interested in capturing and modeling the contextual information, and how a part of this
contextual information is assembled, organized, and structured into learner ontology. Such contextual
information is used to design adaptive systems and to provide customized services to learners based on their
profile and preferences.
In location tracking services, we will match all possible location tracking functionalities currently available for
learners’ devices, then filter them based on learner’s context. For example, if the learner is outside of a building,
then a GPS location tracking function will be invoked to return his/her location in terms of building
name/number; while the learner is inside a building, then indoor tracking system (RFID or sensor network) will
be invoked to return the location in terms of room number. Once the location is positioned, we will decide
whether to disclose the location based on the learner’s privacy preference. Please note, the privacy preference is
dynamic and can be adjusted based on location and temporal constraints. For example, if the learner is inside an
office building, then he/she is willing to disclose the room number where he/she is currently is to the public,
while if the learner is out of office, then the position is only disclosed to his colleagues and family members.