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An Approachable Analytical Study on Big Educational Data Mining

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Computational Science and Its Applications – ICCSA 2014 (ICCSA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8583))

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Abstract

The persistent growth of data in education continues. More institutes now store terabytes and even petabytes of educational data. Data complexity in education is increasing as people store both structured data in relational format and unstructured data such as Word or PDF files, images, videos and geo-spatial data. Indeed learning developers, universities, and other educational sectors confirm that tremendous amount of data captured is in unstructured or semi-structured format. Educators, students, instructors, tutors, research developers and people who deal with educational data are also challenged by the velocity of different data types, organizations as well as institutes that process streaming data such as click streams from web sites, need to update data in real time to serve the right advert or present the right offers to their customers. This analytical study is oriented to the challenges and analysis with big educational data involved with uncovering or extracting knowledge from large data sets by using different educational data mining approaches and techniques.

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Author information

Authors and Affiliations

  1. Department of Information System, University of Malaya, 50603, Pantai Valley, Kuala Lumpur, Malaysia

    Saeed Aghabozorgi, Ashish Dutt, Teh Ying Wah & Tutut Herawan

  2. University of Otago, New Zealand

    Hamidreza Mahroeian

  3. AMCS Research Center, Yogyakarta, Indonesia

    Tutut Herawan

Authors
  1. Saeed Aghabozorgi
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  2. Hamidreza Mahroeian
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  3. Ashish Dutt
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  4. Teh Ying Wah
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  5. Tutut Herawan
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Editor information

Editors and Affiliations

  1. School of Engineering, University of Basilicata, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

  3. Department of Production and Systems, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  4. DICAR, Polytecnico di Bari, 70125, Bari, Italy

    Carmelo Torre

  5. University of Minho, Braga, Portugal

    Jorge Gustavo Rocha  & Maria Irene Falcão  & 

  6. Monash University, 3800,, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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© 2014 Springer International Publishing Switzerland

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Aghabozorgi, S., Mahroeian, H., Dutt, A., Wah, T.Y., Herawan, T. (2014). An Approachable Analytical Study on Big Educational Data Mining. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8583. Springer, Cham. https://doi.org/10.1007/978-3-319-09156-3_50

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  • DOI: https://doi.org/10.1007/978-3-319-09156-3_50

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09155-6

  • Online ISBN: 978-3-319-09156-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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