Published December 27, 2022 | Version v1
Journal article Open

Using Ontological Modeling by Intellectualization of Learning Processes

  • 1. State University of Infrastructure and Technology, Ukraine

Description

The purpose of the article is to investigate and consider the general trends, problems, and prospects of using ontological modeling of learning, training, and education processes in the university.

The research methodology consists in methods of semantic analysis of the basic concepts of the considered subject area (learning, training, and education processes in the university and intelligent technologies). The article discusses approaches to intellectualization education in the university with the help of modern systems that are based on ontological modeling and intelligent technologies.

The scientific novelty of the research is the analysis of the ontological modeling use and intelligent technologies for the intellectualization of learning processes.

Conclusions. The article discusses various aspects related to ontological modeling and intelligent technologies.

The use of ontological modeling in the intellectualization of the higher educational institutions’ activities makes it possible to move to the individualization of learning processes, to provide students and teachers with access to the ontology of not only a separate course, but also all courses of the educational and professional program in the relevant direction, to involve employers and other stakeholders in improving the educational process.

Files

Using_Ontological_Modeling_by_Intellectualization_of_Learning_Processes.pdf

Files (609.4 kB)

Additional details

References

  • Bechhofer, S., 2009. OWL: Web Ontology Language. In: L. Liu and M.T. Özsu, eds. Encyclopedia of Database Systems. Boston: Springer, pp.2008–2009.
  • Gelfert, A., 2017. The Ontology of Models, In: L. Magnani and T. Bertolotti, eds. Springer Handbook of Model-Based Science. Pavia: Springer, pp.5–23.
  • List, C., 2018. Levels: Descriptive, Explanatory, and Ontological. LSE Research Online. [online] Avialable at: <http://eprints.lse.ac.uk/87591/1/List_Levels%20descriptive_2018.pdf> [Accessed 07 April 2022].
  • Lytvyn, V., Vysotska, V., Dosyn, D., Lozynska, O. and Oborska, O., 2018. Methods of Building Intelligent Decision Support Systems Based on Adaptive Ontology. In: IEEE Second International Conference on Data Stream Mining & Processing (DSMP). Lviv, Ukraine, 21-25 August 2018. Lviv, pp.145–150.
  • Musen, M.A., 2015. The Protégé Project: A Look Back and a Look Forward. AI Matters, 1(4), pp.4–12.
  • Protégé 5.5. , 2016. Protégé Project, [online] 19 May. Avialable at: <https://protege.stanford.edu/products.php> [Accessed 07 April 2022].
  • Sanfilippo, E.M., 2018. Feature-based product modelling: an ontological approach. International Journal of Computer Integrated Manufacturing, 31 (11), pp.1097–1110.
  • Тkachenko, O.I., Тkachenko, O.A. and Тkachenko, К.O., 2020. Designing Complex Intelligent Systems on the Basis of Ontological Models. In: CMIS-2020: Third International Workshop on Computer Modeling and Intelligent Systems. Zaporozhye, Ukraine, [online] 27 April-1 May 2020. Zaporozhye, pp.266–277. Available at: <http://ceur-ws.org/Vol-2608/paper20.pdf> [Accessed 24 April 2022].