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Flexible Model for Organizing Blended and Distance Learning

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

The proposed flexible model for the organization of mixed and distance learning involves the creation of an individual learning path for student testing before starting training. Based on the learning outcomes, the student is credited to the learning path. The learning path consists of mandatory and optional modules for training, optional modules can be skipped by passing the test successfully. The training model is represented using the ontological model, and the decision-making rules for the model are logical rules.

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Author information

Authors and Affiliations

  1. L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan

    Gulmira Bekmanova & Yerkin Ongarbayev

Authors
  1. Gulmira Bekmanova
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  2. Yerkin Ongarbayev
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Corresponding author

Correspondence to Gulmira Bekmanova .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Bekmanova, G., Ongarbayev, Y. (2020). Flexible Model for Organizing Blended and Distance Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-58802-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58801-4

  • Online ISBN: 978-3-030-58802-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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