Abstract—Currently, much of the courseware and resources in Adaptive Education Hypermedia are unstructured and isolated from each other thereby lacking in quality too. Therefore , this research aims to provide a research framework to develop a system for the learning process which is able to store the quality content and depending on cognitive state of the student, preset a qualitative and quantitative result of their learning level. Thus, the objective of this research paper is threefolds. It firstly looks at the available literature related to tools techniques for Learning Object(LO) Evaluation and metrics Secondly, it proposes a framework for the design and dissemination of Adaptive education combining learning theory with LO evaluation systems. The paper finally concludes by identifying a relationship between the determined learning style profile, the assigned task and the chosen representation of the content. Adaptive Education Hypermedia (AEH) is a challenging research area that helps to improve the learning of the students adjusting the content and navigation alternatives to their characteristics. It is a process where learners gain knowledge and skills interacting with learning resources, activities and other students. Learning design (1) details this process, considering learning goals, prerequisites and expected outcomes to indicate learning activities, sequencing and learning materials. Advanced techniques are already being used in higher education to facilitate learning and teaching, but inadequacies still exist (2). Currently, much of the courseware and resources are unstructured and isolated from each other (3). The genesis of adaptive learning systems is from the artificial intelligence (AI) research. In the early 1980 there was significant development of systems to provide intelligent response to users interacting with the computers. The early AI research developed into three overlapping streams, namely, knowledge based expert systems, neural networks and genetic algorithms. These technologies were used primarily in adaptive control systems that managed the difficult task of controlling electromechanical actuators to adapt to the given situation and respond accordingly. The artificial intelligence systems were based on strategies to learn user's behavior and respond accordingly. The conceptual and philosophical differences of these approaches led to the learning systems that were either influenced by the connectionists model that created supervised neural nets or unsupervised self organizing