ENTROPY BASED PERSONALIZED LEARNING MANAGEMENT SYSTEM (PELMS) – AN APPROACH TOWARDS BUSINESS AND IT EDUCATION
Keywords:
Personalized Learning Management System (PeLMS), Semantic Web (SW), Learning Content Model, Semantic Overlay Network (SON), Learning Object Repositories (LOR), Entropy, Ontology and Annotation, Business and IT EducationAbstract
Development of personalized e-Learning Management Systems (PeLMS) using advance modelling techniques is crucial to achieve personalization and adaptation in content deliveries. This paper delves on some important issues related to the integration of PeLMS with Semantic Overlay Networks (SON). Developing a prototype for a Business Statistics course delivery, a Semantic Web based PeLMS using Topic Maps, Ontology, Classification rules, and ISA Algorithm has been prescribed. Considering classification as one of the important aspects in the content organisation in PeLMS, this study proposes a new classifier, based upon the maximum entropy principle. It is argued that the most similar items in a learning object repository space can be classified together, on the statistical basis, to build a PeLMS. The ISA algorithm has been proposed to enable this classification. The paper also presents three key Learning Object Models for the organization of contents and suggests how an optimal level of personalization that can be ensured by maintaining the entropy within the system. Mechanisms such as normalization and time complexity have also been suggested to ensure personalized and optimal content delivery.
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