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E-Learning Data Mining

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e-Learning, e-Education, and Online Training (eLEOT 2018)

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Abstract

In order to effectively analyze a large number of original data, the concept of large data is proposed. Big data era has brought a new change for education. With the rapid development of education information especially digital campus network and the growing popularity of higher education, education in the field of the deployment of many of the software systems, software systems in these stores massive educational data. E-Learning produced a large number of educational data in a variety of forms. E-Learning has achieved a certain effect on the scale effect, but the support for the individual of the learners is still seriously inadequate. The new technology, represented by large data and social networks, provides a new possibility for personalized learning.

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References

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Authors and Affiliations

  1. School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Yanqing Wang

Authors
  1. Yanqing Wang
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Corresponding author

Correspondence to Yanqing Wang .

Editor information

Editors and Affiliations

  1. Inner Mongolia University, Hohhot, China

    Shuai Liu

  2. Quinn School of Business, University College Dublin, Dublin, Ireland

    Matt Glowatz

  3. University of Salento, Poggiardo, Lecce, Italy

    Marco Zappatore

  4. Shanghai University, Shanghai, China

    Honghao Gao

  5. Inner Mongolia Agricultural University, Hohhot, China

    Bing Jia

  6. IBAM - Istituto per i Beni, Italian National Research Council, Rome, Italy

    Alberto Bucciero

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, Y. (2018). E-Learning Data Mining. In: Liu, S., Glowatz, M., Zappatore, M., Gao, H., Jia, B., Bucciero, A. (eds) e-Learning, e-Education, and Online Training. eLEOT 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 243. Springer, Cham. https://doi.org/10.1007/978-3-319-93719-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-93719-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93718-2

  • Online ISBN: 978-3-319-93719-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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