A recommender system based on hierarchical clustering for cloud e-learning

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Abstract

Cloud e-Learning (CeL) is a new paradigm for e-Learning, aiming towards using any possible learning object from the cloud in a smart way and generate a personalised learning path for individual learners. An issue that appears before the generation of the learning path through automated planning, is to filter a pool of resources that are relevant to the learners profile and desires in order to enhance their knowledge and skills at a higher cognitive level. In this paper, we present a Recommender System for Cloud e-Leaning (CeLRS) that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. We discuss the issues raised and we demonstrate how CeLRS works.

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Pireva, K., & Kefalas, P. (2017). A recommender system based on hierarchical clustering for cloud e-learning. Studies in Computational Intelligence, 737, 235–245. https://doi.org/10.1007/978-3-319-66379-1_21

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