PeRES: A personalized recommendation education system based on multi-agents & SCORM

Most E-Learning models proposed recently can offer personalized learning services for learners or make courseware reusable or portable. However, there are few models that can serve both purpose and none of them is designed to provide personalized services for both learners and instructors. This paper introduces an architecture of school-based personalized recommendation education system which can provide personalized services not only for diverse learners but also for instructors. In addition, it offers reusability and interoperability of courseware that is conformant with SCORM 2004 3rd Edition specifications. The architecture adopts multi-agents technology and consists of SIX software agents, which coordinate work hierarchy with each other to offer a range of primary functions that include static and dynamic users modeling, learning plan generation and adjustment, personalized content search, personalized recommendation, as well as real-time evaluation of learning progress. We provide the detail functional specification of these agents as well as a scenario walk-through of the architecture.