Abstract
With the rapid development of Web 2.0 and interactive technologies, learning resources are proliferating online. Confronting such large volume of educational data, users require effective and efficient methodologies to organize and manage them, which reveals the importance of clustering. In this paper, we first propose a method to estimate the data density, and then apply it to merge learning resources. The proposed algorithm estimates the confidence of any two learning resources to be a pair of neighbors, and conducts clustering by combining the above confidence with the similarities among resources. Experiments are designed to evaluate the performance of our algorithm using the standard clustering datasets. We also demonstrate how to employ the proposed algorithm in educational applications, including e-learner grouping, resource recommendation and usage patterns discovery.
Y. Rao—The research work described in this article has been substantially supported by the Fundamental Research Funds for the Central Universities (Project Number: 46000-31610009).
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Acknowledgements
The research described in this paper has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).
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Wang, Z., Kang, P., Wu, Z., Rao, Y., Wang, F.L. (2016). A Density-Based Clustering Algorithm with Educational Applications. In: Gong, Z., Chiu, D., Zou, D. (eds) Current Developments in Web Based Learning. ICWL 2015. Lecture Notes in Computer Science(), vol 9584. Springer, Cham. https://doi.org/10.1007/978-3-319-32865-2_13
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