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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.

The desire to improve contemporary education has led many educators into practices that are amalgamations of different learning and pedagogical theories. Despite government interventions, new pedagogies, and the ever-contentious role of technology, the need for improvement in education remains as students still underperform. This has led to the authors' hypothesis that the personalization of the structure, content, and delivery method of curricula is an effective and readily applicable way for technology to improve learning. The purpose of this chapter is to discuss the authors' early attempts at proving this hypothesis by presenting the methods they have developed. It starts by providing motivation and background research that has influenced their work. The remainder of the chapter discusses the design and implementation of PERCEPOLIS: a pervasive cyberinfrastructure for personalized education and instructional support.