ISSN 2782-4594 (Print)
ISSN 2782-4608 (Online)


For citation:

Sleptsova M. V., Sleptsova N. A. Physical education in E-learning. Physical Education and University Sport, 2022, vol. 1, iss. 2, pp. 189-198. DOI: 10.18500/2782-4594-2022-1-2-189-198, EDN: CKQHCK

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Language: 
Russian
Article type: 
Article
UDC: 
37.01
EDN: 
CKQHCK

Physical education in E-learning

Autors: 
Sleptsova Marina V., Plekhanov Russian University of Economics
Sleptsova Nadezhda A., Voronezh State Pedagogical University
Abstract: 

Forming the need for a healthy lifestyle, the transfer of knowledge, skills and abilities in the field of physical culture and sports are an urgent pedagogical problem in the transition from the “classical” forms of organization of the educational process to E-learning. The solution of this problem by means of well-known programming languages is impossible due to the insufficient descriptive potential of the latter, in particular, the lack of the possibility of representing human knowledge, probable or possible future events, actions, results. The purpose of our research is to develop theoretical provisions and to verify in practice the possibilities of formalizing the presentation of the individual educational goal of the student and the conscious need for a healthy lifestyle as its basic component in declarative languages, as well as its implementation by means of the educational platform of the university. The researcher considers the possibility of describing the individual educational goal of a student, including a conscious need for a healthy lifestyle as its basic component, gaining knowledge, forming and developing skills in the field of physical culture and sports, as well as the possibility of building an individual educational trajectory of its achievements in the organization of E-learning at the university in the language of first-order predicate logic. A number of features of the predicate logic language that are essential for the problem under consideration are singled out. A pedagogical experiment was conducted on the basis of the educational platform of Voronezh State Pedagogical University. The study involved 158 students and undergraduates. The descriptions of individual educational goals of the student, formalized by the language of logic of predicates, suitable for implementation by means of computer technology are obtained, on the basis of which the optimal trajectory for achieving this goal is calculated for each participant. The practical implementation of the educational process was carried out, data on a conscious need for a healthy lifestyle, gaining knowledge, forming and developing skills in the field of physical culture and sports were obtained. The obtained results were compared with similar studies and a conclusion was made about the ways to improve the suggested approach to the formation of a conscious need for students to lead a healthy lifestyle, gain knowledge, form and develop their skills in the field of physical culture and sports in the context of the transition from “classical” forms of organization of the educational process to E-learning.

Reference: 
  1. Filimonova S. I., Andrjushhenko L. B., Almazova Ju. B., Puhovskaja M. N., Slepcova M. V. Preparation of students for passing the standards of the VFSK GTO in the educational environment of the university. Innovative technologies in sports and physical education of the younger generation : collection of articles based on the materials of the X scientific and practical conference with international participation, Moscow, May 14–15, 2020. Moscow, Moscow City Pedagogical University, 2020, pp. 409–415 (in Russian).
  2. Bukova L. M., Bukov Yu. A., Andryushchenko L. B., Kobza M. Efficient academic PE service model to improve rehabilitation and work efficiency. Theory and Practice of Physical Culture, 2019, no. 9, pp. 18–20 (in Russian).
  3. Filimonova S. I., Andryushchenko L. B., Aksenov M. O. Fizicheskaya kul’tura [Physical Culture]. Moscow, Russian University of Economics named after G. V. Plekhanov, 2021. 272 p. (in Russian).
  4. Grigorenko E. V. Philosophical concepts of natural language. Society: Philosophy, History, Culture, 2019, no. 7 (63), pp. 27–31 (in Russian). https://doi.org/10.24158/ fik.2019.7.4
  5. Rozanov A. K., Prutskov A. V. Algorithms and knowledge structures for pre-syntax analysis of natural language texts. Cloud of Science, 2017, № 3, pp. 415–433 (in Russian).
  6. Yasulova Kh. S. The model of the formal grammar of the place and direct additions and the possibility of its use in artificial intelligence systems. System Technologies, 2018, no. 3 (28), pp. 93–99 (in Russian).
  7. Halliday M. A. K. Aspects of Language and Learning. Springer-Verlag Berlin Heidelberg, 2016. 149 p. https://doi.org/10.1007/978-3-662-47821-9
  8. Kapetanios E., Tatar D., Sacarea C. Natural Language Processing: Semantic Aspects. CRC Press, 2013. 346 p. https://doi.org/10.1201/b15472
  9. Karelin V. P. Models and methods of knowledge representation and decision making in intelligent information systems with fuzzy logic. Bulletin of the Taganrog Institute of Management and Economics, 2014, no. 1 (19), pp. 75–83 (in Russian).
  10. Sologub G. B. Construction and use of the BASE network for modeling student knowledge in an intelligent testing system. Computer Tools in Education, 2012, no. 2, pp. 40–48 (in Russian).
  11. Shikhnabieva T. Sh. Methods and models of knowledge representation in integrated intellectual systems of educational significance. Modern Pedagogical Education, 2017, no. 3, pp. 3–15 (in Russian).
  12. Shumkov E. A. Frame expert systems using neural networks. Polythematic Online Scientific Journal of Kuban State Agrarian University, 2019, no. 154, pp. 226–232 (in Russian). https://doi.org/10.21515/1990-4665-154-021
  13. Sleptsova M. V., Filimonova S. I., Andrjushhenko L. B., Galochkin P. V. Predicate-logics-based education model to form individual physical progress agenda. Theory and Practice of Physical Culture, 2022, no. 3, pp. 61–63 (in Russian).
  14. Sleptsova N. A. The language of pedagogical design. Aktual’nye problemy, sovremennye tendentsii razvitija fizicheskoj kul’tury i sporta s uchetom realizacii natsional’nykh proektov : materialy III Vserossijskoj nauchno-prakticheskoj konferentsii s mezhdunarodnym uchastiem, Moskva, 22–23 aprelja 2021 goda [Actual problems, modern trends in the development of physical culture and sports, taking into account the implementation of national projects : Proceedings of the III All-Russian scientific and practical conference with international participation, Moscow, April 22–23, 2021]. Moscow, Russian University of Economics named after G. V. Plekhanov, 2021, pp. 1341–1345 (in Russian).
  15. Sleptsova N. A. Pedagogical category “motivationalvalue attitude to health”. In: Sportivnaja nauka. Innovatsii v obrazovanii: materialy studencheskoj nauchno prakticheskoj konferentsii, Moskva, 8 dekabrja 2021 goda [Sports science. Innovations in education: materials of the student scientific and practical conference, Moscow, December 08, 2021]. Moscow, Russian University of Economics named after G. V. Plekhanov, 2021, pp. 237–247 (in Russian).
  16. Nevorotov B. K., Moiseev M. B. Modeling of information structures in organization of educational activity. Omsk Scientific Bulletin, 2015, no. 3 (139), pp. 108–112 (in Russian).
  17. Shevchenko V. A., Kudin A. I. Construction of models of students’ behavior based on matrixes of fuzzy relations considering their motivations to improve success in learning. Bulletin of Kharkov National Automobile and Highway University, 2019, no. 85, pp. 7–13 (in Russian). https://doi.org/10.30977/BUL.2219-5548.2019.85.0.7
  18. Sleptsova M., Sokolova N., Shamanina L., Gubanova I. Formalization of the pedagogical model by the language of predicate logic. Advances in Social Science, Education and Humanities Research, 2020, vol. 396 : International Scientific and Practical Conference on Education, Health and Human Wellbeing (ICEDER 2019), pp. 19–23. https://doi.org/10.2991/iceder-19.2020.5
  19. Sotiropoulos D., Tsihrintzis G. Machine Learning Paradigms. Springer International Publishing AG, 2017. 327 p. https://doi.org/10.1007/978-3-319-47194-5
Received: 
03.06.2022
Accepted: 
11.06.2022
Published: 
07.11.2022