Accuracy of Retrieval Files in Learning Objects using Cloud E-Learning - Akram, Faiz; Kumar, Rajeev (International Journal of Computer Sciences and Engineering, 2018)
Everything stored on the cloud could potentially be a knowledge source used for e-learning. Given learners' profiles, desires and feedback on what they have already learned, a new form of personalized e-learning emerges, namely Cloud E-Learning (CeL). CeL should be able to choose from structured to totally unstructured learning material but needs to make them useful for each individual. Existing metadata standards cannot facilitate composition of personalized learning paths as a series of learning objects. In this paper, we present the structure of CeL Learning Objects (CeLLOs), which include an additional set of metadata suitable for each phase of CeL development.