Adaptive content presentation in asynchronous learning environments

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.