The Electronic Journal of e-Learning provides perspectives on topics relevant to the study, implementation and management of e-Learning initiatives
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Journal Issue
Volume 13 Issue 1 / Jan 2015  pp1‑56

Editor: Rikke Ørngreen, Karin Levinsen

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Editorial.pdf  pp1‑2

Karin Levinsen, Rikke Ørngreen

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Knowledge Enriched Learning by Converging Knowledge Object & Learning Object  pp3‑13

Sai Sabitha, Deepti Mehrotra, Abhay Bansal

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Abstract: The most important dimension of learning is the content, and an LMS suffices this to a certain extent. The present day LMS are designed to primarily address issues like ease of use, search, content and performance. Many surveys had been conducte d to identify the essential features required for the improvement of LMS, which includes flexibility and a user centric approach. These features can suffice the need of all learners, when they have different learning requirements. For a true learning, kno wledge should also be delivered along with the domain information. Thus, there is a need to design an architecture for user centric Knowledge Driven Learning Management System. Thus for holistic learning, knowledge enriched teaching skills are required, w hich can enhance and increase the thinking skills of the learner to a higher level. The current LMS needs an improvement in the direction of knowledge discovery, exploration so that knowledge enriched learning can be provided to the learner.. It can be ba sed on knowledge engineering principles like ontology, semantic relationship between objects, cognitive approach and data mining techniques. In this paper, we are proposing an idea of an enhanced Learning Object (LO) called Knowledge Driven Learning Obj ect, which can be delivered to the user for better learning. We had used a data mining approach, classification to harness and exploit these objects and classify them according to their metadata, thereby strengthening the content of objects delivered thro ugh the LMS. 


Keywords: Keywords: Learning object, knowledge objects, lms, kms, classification, decision tree, knowledge driven learning objects, knowledge driven learning management system, e-learning


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