There is a crucial issue in Adaptive Educational

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Abstract-In Adaptive Educational Hypermedia Systems (AEHS), we expect that the learning content presentation should be appropriately retrieved from learning object repositories, and dynamically tailored to each learner's needs. Each learner has a profile, subject to continuous change. The basic components of the learner's profile include his/her cognitive characteristics, background of knowledge, previous experience, and current emotional situation. This paper proposes the architecture of a Petri net-based workflow engine -scheduled to be implemented in a AEHS -aiming to provide a reliable and efficient platform for the execution of learning course flows in a grid environment. Dealing with the question of adaptive management of learning content, the proposed p-timed Petri net is capable of presenting learning content adapted to the learner's Learning Style and knowledge background. The proposed schema is accurately tested using a p-timed Petri net simulator. The schema may now be extended to include other components of the learner's profile.

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