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International Conference on Computational Science and Its Applications

ICCSA 2020: Computational Science and Its Applications – ICCSA 2020 pp 282–292Cite as

Flexible Model for Organizing Blended and Distance Learning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12250))

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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References

  1. Conole, G., de Laat, M., Darby, J.: Disruptive technologies’, ‘pedagogical innovation’: what’s new? findings from an in-depth study of students’ use and perception of technology. Comput. Educ. 50(2), 511–524 (2008)

    Article  Google Scholar 

  2. Dias, S.B., Diniz, J.A., Hadjileontiadis, L.J.: Towards an Intelligent Learning Management System Under Blended Learning Trends, Profiles and Modeling Perspectives. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02078-5

    Book  Google Scholar 

  3. Redecker, C., Ala-Mutka K., Bacigalupo M., Ferrari A., Punie, Y.: Learning 2.0: The Impact of web 2.0 Innovations on Education and Training in Europe. Joint Research Centre (JRC)-Institute for Prospective Technological Studies (2009). http://is.jrc.ec.europa.eu/pages/Learning-2.0.html

  4. Sharipbay, A., Barlybayev, A., Sabyrov, T.: Measure the usability of graphical user interface. In: New Advances in Information Systems and Technologies. AISC, vol. 444, pp. 1037–1045. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31232-3_98

  5. Sharipbay, A., Omarbekova, A., Seifullina, A., Zakirova, A.: Creating intelligent electronic textbooks based on knowledge management. In: The World Congress on Engineering 2014, vol. 1, pp. 224–227 (2014)

    Google Scholar 

  6. Sharipbay, A., Omarbekova, A., Nurgazinova, G.: Creating intelligent electronic textbooks in Kazakhstan. In: 6th International Conference on Education and New Learning Technologies. Barcelona/Spain, pp. 2926–2933 (2014)

    Google Scholar 

  7. Nurgazinova, G., Golenkov, V., Sharipbay, A., Omarbekova, A., Barlybayev, A.: Designing of intelligent reference system for algebra based on the knowledge database. In: International Conference on Control, Automation and Artificial Intelligence, pp. 230–235 (2015)

    Google Scholar 

  8. Köse, U., Deperlioglu, O.: Intelligent learning environments wthin blended learning for ensuring effective C programming course. Int. J. Artif. Intell. Appl. 3 (2012). https://doi.org/10.5121/ijaia.2012.3109

  9. Yigit, T., Koyun, A., Yüksel, A., Cankaya, I., Köse, U.: An Example Application of Artificial Intelligence Supported Blended Learning Education Program in Computer Engineering (2014). https://doi.org/10.4018/978-1-4666-6276-6.ch012

  10. Newell, D., Davies, P., Austin, R., Moore, P., Sharma, M.: Models for an intelligent context-aware blended m-learning system. In: 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, Gwangiu, pp. 405–410 (2015). https://doi.org/10.1109/waina.2015.25

  11. Yergesh, B., Mukanova, A., Sharipbay, A., Bekmanova, G., Razakhova, B.: Semantic hyper-graph based representation of nouns in the Kazakh language. Computacion y Sistemas 18(3), 627–635 (2014)

    Google Scholar 

  12. Bekmanova, G., Sharipbay, A., Omarbekova, A., Yelibayeva, G., Yergesh, B.: Adequate assessment of the customers actual reviews through comparing them with the fake ones. In: Proceedings of 2017 International Conference on Engineering and Technology, pp. 1–4 (2018)

    Google Scholar 

  13. Mukanova, A., Yergesh, B., Bekmanova, G., Razakhova, B., Sharipbay, A.: Formal models of nouns in the Kazakh language. Leonardo Electron. J. Pract. Technol. 13(25), 264–273 (2014)

    Google Scholar 

  14. Bazarova, M., Zhomartkyzy, G.: Ontological model of professional competences for ICT-specilaists 6D070300. Vestnik KazNITU. №1 (119), pp. 321–328 (2017)

    Google Scholar 

  15. Zulfiya, K., Gulmira, B., Altynbek, S.: Ontological model for student’s knowledge assessment. Paper Presented at the ACM International Conference Proceeding Series (2019)

    Google Scholar 

  16. Yergesh, B., Bekmanova, G., Sharipbay, A., Yergesh, B.: Ontology-Based Sentiment Analysis of Kazakh Sentences (2017)

    Google Scholar 

  17. Gaeta, M., Orciuoli, F., Ritrovato, P.: Advanced ontology management system for personalised e-Learning. Knowl. Based Syst. 22(4), 292–301 (2009)

    Article  Google Scholar 

  18. Liu, B.L., Hu, B., Wang, X.Y., Zheng, L.: A RDF-based knowledge base implementation for E-learning. New horizon in web-based learning. In: 3rd International Conference on Web-Based Learning, pp. 45–50 (2004)

    Google Scholar 

  19. Wang, L., Zhang, Yu., Liu, T.: A deep learning approach for question answering over knowledge base. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 885–892. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_82

    Chapter  Google Scholar 

  20. Omarbekova, A., Sharipbay, A., Barlybaev, B.: Generation of test questions from RDF files using PYTHON and SPARQL. J. Phys. Conf. Ser. 806(1) (2017)

    Google Scholar 

  21. Omarbekova, A., Nurgazinova, G., Sharipbay, A., Barlybayev, A., Bekmanova, G.: Automatic formation of questions and answers on the basis of the knowledge base. In: Paper presented at the Proceedings of 2017 International Conference on Engineering and Technology (2018)

    Google Scholar 

  22. Yelibayeva, G., Mukanova, A., Sharipbay, A., Zulkhazhav, A., Yergesh, B., Bekmanova, G.: Metalanguage and knowledgebase for Kazakh morphology. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11619, pp. 693–706. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24289-3_51

    Chapter  Google Scholar 

  23. Sharipbayev, A., Bekmanova, G., Yergesh, B., Mukanova, A., Buribayeva, A.: Semantic retrieval of information in the Kazakh language in elibraries. J. Int. Sci. Publ. Educ. Alternat. 10(1), 108–115 (2014)

    Google Scholar 

  24. Barlybayev, A., Kaderkeyeva, Z., Bekmanova, G., Sharipbay, A., Omarbekova, A., Altynbek, S.: Intelligent System for Evaluating the Level of Formation of Professional Competencies of Students (2020)

    Google Scholar 

  25. Zulfiya, K., Gulmira, B., Altynbek, S., Assel, O.: A model and a method for assessing students’ competencies in e-learning system. Paper Presented at the ACM International Conference Proceeding Series (2019)

    Google Scholar 

  26. https://protege.stanford.edu/

  27. https://www.microsoft.com/en-us/digitalliteracy/home

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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

  • Published:

  • 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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