Several Learning Content Aggregation Models including SCORM have been evolved around different Learning Object (LO) architecture. These models seem to characterize their LOs with attributes to support e-Learning mainly towards the perspective of content aggregation. None of these models has considered the attributes specially to support personalization aspect of e-Learning. With the motivation of developing a Learning Content Aggregation Model that is capable of providing personalized learning service to the e-Learning audience a novel LO-architectural model called RMLOM (Reusable and Multipurpose Learning Object Model) is proposed. The LO-attributes are extended to allow each LO reconfigurable to multiple purposes including personalized service. The RMLOM Content Aggregation model has seven layers of component aggregation namely Learning Material Object (LMO: Unstructured learning information), Data Object (DO: structured information for learning), Reusable Multipurpose Learning Object (RMLO: structured information for personalized learning), Concept, Chapter, Course and Degree. Actually a DO is equivalent to an LO in other models and a RMLO is an extended LO built using DOs. RMLO can be configured with a set of DOs depending upon the application criteria, specifically to suit personalized learning. Based on this extended LO model (RMLOM) a prototype Content Aggregation System has been implemented. The research in this thesis mainly concentrated on the design of RMLOM framework and development of a content management system around this framework. This project has four main objectives. First objective is to review the work done in the literature on Learning Objects in order to have a comprehensive understanding of what this term represents. Secondly, it is to figure out the existing content aggregation models that are based on Learning Objects. Thirdly, it is to develop RMLOM-based framework that supports Personalized Learning and a Learning Object Repository (LOR) that can provide a variety of LOs for various types of Learners. Last objective is to compare the features of the developed system with a similar system in establishing its functional capability.