Abstract
The paper proposes an E-library which will be combining the collective intelligence of the social web with E-learning. In this paper, we argue that though it is important to have a structured explicit knowledge base in an E-learning site, in the present scenario when lot of research is being done on the personalized E-learning, it is necessary to refine the repository with the tacit knowledge which is unstructured and implicitly resides in people. It can be utilized from socialization, feedbacks, reviews, blogs etc. The paper suggests an e-library system where a derived structured knowledge base will be generated from both explicit and tacit knowledge of the learners and in return will give learner a targeted, updated and quality material to refer. In this paper, we have designed an E-library which will be user oriented and will estimate the utility of every resource for a given user based on two metrics Level and Like. It also provides a feedback system for modifying the metrics with the user response. It considers the effect of expert biasing and also diversity of collective intelligence.
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Jindal, R., Singhal, A. (2017). Personalized E-library: A Recommender System Based on Learner’s Feedback Model. In: Basu, A., Das, S., Horain, P., Bhattacharya, S. (eds) Intelligent Human Computer Interaction. IHCI 2016. Lecture Notes in Computer Science(), vol 10127. Springer, Cham. https://doi.org/10.1007/978-3-319-52503-7_20
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DOI: https://doi.org/10.1007/978-3-319-52503-7_20
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