New Weighted Clustering Approach to Map and Prioritise Learning Knowledge Objects towards Learning Approaches

Sai Sabitha, Deepti Mehrotra, Abhay Bansal

Abstract


To have a unique learning experience and a high learning impact, diverse courses should be incorporated in e-Learning. Learning Management System, a tool in e-Learning manages and delivers content to users. Learning Objects (LO), the course content is the fundamental unit of Learning Management System. Knowledge Object of Knowledge Management System can also be a viable resource in technology supported learning. A learning scenario for a given learner has to be identified. The course content (LO) has to match their learning skills. Data mining techniques can be widely used to find similar objects and K-Mean clustering technique can be used to produce more consistent clusters. The clusters can have strong and similar concepts of Learning Knowledge Objects. A new algorithm, a weighted cosine distance that gives real-valued distances between instances which further modifies the structure of the feature space is used for prioritising objects in clusters. These objects can be further mapped to learning approaches of the users. An experiment is conducted by using Learning and Knowledge Objects to understand the effectiveness of the weighted measure, thereby a personalized holistic learning environment is provided to the learners.

Keywords


Deep Learning; K-Mean; Knowledge Objects; Learning Approaches; Learning Object; Metadata;

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International Journal of Emerging Technologies in Learning. ISSN: 1863-0383
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