In the present scenario of educational technology inter-networking have provided a platform to access and share learning materials spread across various educational institutions. E-learning platforms provide an interface for accessing and sharing of heterogeneous educational resources (Learning Materials) to the various types of learners and content providers. These materials are created as the smallest digital imprints called as learning objects for the better usability. However, due to the variation in learner's competence, providing a right content to the learner has become a cumbersome task. Consequently, a lot of personalization towards the creation and storage of the learning objects has become obligatory. Strategies involving learning style detections have provided a source of solution for the personalization. Yet, these methods are carried out with a limited number of learning styles and learning objects. And, most of these methods fail to upgrade the competency level of the learners as it provides less concentration to the learner's capability. In order to personalize the system in consideration of the learner's capability demands, there is a demand to understand the individual learner's strength and weakness in the learning process. One of the features that decide the learner's capability is the cognitive skill of the learners. It is desirable to maintain e-learning materials with respect to cognitive skill of the learners so the learning process becomes enjoyable. This paper focuses on the grouping of the e-materials with respect to the dominant cognitive content of learning objects. Hence, an organized storage mechanism is envisioned to aid the faster search and recovery of learning objects depending on the individual's capabilities. The process is aimed to classify the materials that could be stored in a dedicated repository maintained in a distributed environment for a good usability. © 2019-IOS Press and the authors. All rights reserved.