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Mathematical Models of Learning Materials Estimation Based on Subject Ontology

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Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 450))

Abstract

Two mathematical models are under consideration in this paper: the ontology usage as a tool of automation has become popular nowadays. There are comparisons between the subject ontology’s part and students’ cognitive map which are being developing during the test. It gives a possibility to assess student’s knowledge skills as well as some new characters of the educational outcomes. The core competencies form the basis of the student’s ability to learn, as well as interdisciplinary concepts or metaconcepts. The first and second sections of the article present a subject ontology model. The third section is devoted to metaconcepts testing based on metasubject’ ontology. In conclusion we formulate our main results and prospects of its development.

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Notes

  1. 1.

    SCM—Student cognitive map.

  2. 2.

    SSN—Subject semantic network.

  3. 3.

    CML—Cognitive map learning.

  4. 4.

    SN—Semantic network.

  5. 5.

    MSN—Metasubject ontology.

References

  1. Cruz, B.A., Eremeeva, E.V.: The definition of competencies metasubject junior student. Mod. Probl. Sci. Educ. 6 (2013). http://www.science-education.ru/ru/article/view?id=11014

  2. Belous, V.V., Karpenko, A.P., Smirnova, E.V.: Evaluation of conceptual knowledge of the student based on hierarchical clustering role. Educ. Sci.: Sci. Tech. Electron. Ed. 9 (2014). http://technomag.bmstu.ru/doc/726237.html

  3. Avdeeva, Z.K., Kovriga, S.V., Makarenko, D.I., Maksimov, V.I.: The cognitive approach in the management. Probl. Upr. 3, 2–8 (2007)

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  4. Ontology conceptual data models. http://www.cambridgesemantics.com/resources/blog/ontologies-conceptual-models

  5. Greshilova, A.V.: The content of interdisciplinary competences of secondary professional education students. Magister Dixit 1, 13 (2014). http://md.islu.ru/sites/md.islu.ru/files/rar/greshilova_statya_md_0.pdf

  6. Scharnhorst, A., Ebeling, W.: Evolutionary search agents in complex landscapes—a new model for the role of competence and meta-competence (EVOLINO and other simulation tools). The Virtual Knowledge Studio: website. http://virtualknowledgestudio.nl/documents/_andreascharnhorst/arxiv_final.pdf

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Authors and Affiliations

  1. Bauman State Technical University, Moscow, Russia

    M. A. Shpak, E. V. Smirnova, A. P. Karpenko & A. V. Proletarsky

Authors
  1. M. A. Shpak
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  2. E. V. Smirnova
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  3. A. P. Karpenko
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  4. A. V. Proletarsky
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Corresponding author

Correspondence to E. V. Smirnova .

Editor information

Editors and Affiliations

  1. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  3. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  4. Institute of Mathematics, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Václav Snášel

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© 2016 Springer International Publishing Switzerland

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Shpak, M.A., Smirnova, E.V., Karpenko, A.P., Proletarsky, A.V. (2016). Mathematical Models of Learning Materials Estimation Based on Subject Ontology. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Advances in Intelligent Systems and Computing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-33609-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-33609-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33608-4

  • Online ISBN: 978-3-319-33609-1

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