A naive bayes approach for converging learning objects with open educational resources

Open educational resources (OER) are digitised material freely available to the students and self learners. Many institutions had initiated in incorporating these OERs in their higher educational system, to improve the quality of teaching and learning. These resources promotes individualised study, collaborative learning. If they are coupled with Learning Objects of Learning Management System (LMS), they can lead to opportunities for further pedagogical innovation. It has become increasingly important for educational institutions to support these resources, in a planned and systematic manner. Adapt, assemble and conceptualise existing OERs to respond to diverse learning needs of students and support a variety of learning approaches for a given learning goal is a challenge. In this work, convergence of OERs with Learning Objects is done through metadata using classification techniques. Localisation of these high quality learning materials with the learning content of LMS, delivered as a single instructional unit may help in greater knowledge delivery and this can satisfy the learning needs of diverse student.

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