Pervasive computing has the potential to dramatically increase the efficacy of current pedagogical practices, but it is sorely underutilized. Personalized learning, which has been identified as an engineering grand challenge for the coming century, can benefit from the ability of pervasive computing to transparently collect and utilize information about a student's background, interests, and needs. The objective of the doctoral research described in this paper is to leverage pervasive computing to develop context-aware ontologies and recommendation algorithms for personalized learning, extending, enriching, and refining the pervasive computing that serves as the backbone of PERCEPOLIS - Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support.
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