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Education individualization by means of artificial neural networks

Valko, Nataliia; Osadchyi, Viacheslav

URI: https://www.e3s-conferences.org/articles/e3sconf/abs/2020/26/e3sconf_icsf2020_10021/e3sconf_icsf2020_10021.html
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3772
https://doi.org/10.1051/e3sconf/202016610021

Date: 2020-04-22

Abstract:

This paper examines the issues related to the implementation of an educational process based on modern information technologies use. The main purpose of it is to achieve a significant level of individualization of the educational process, taking into account the individual characteristics and capabilities of each participant of the process. The implementation of the approach became possible at using elements of the theory of artificial neural networks in the educational process. Based on the network, it is possible to build a model of the educational process; it will significantly increase the control of the teacher on the educational process. Moreover, this network can adapt to a specific education task, the individual characteristics of the student and teacher. The mathematical model of the educational process using modern information technologies and neural networks is constructed. Their use is based on the developed criteria of successful execution of various stages of the educational process. Such criteria are designed for both the student and the teacher. The characteristic of participant’s activity of the educational process is considered in the work. A numerical interpretation of this concept is proposed.

Description:

1. G.V. Lutsenko, L.V. Kozulya, Analiz osoblyvostey vprovadzhennya problemno-oriyentovanoho navchannya u systemi vyshchoyi osvity Ukrayiny (Analysis of peculiarities of implementation of problem-oriented education in the higher education system of Ukraine). Visn. Chernihiv. nat. ped. un. Ped. Sciences. 138, 91–95 (2016) 2. V.S. Kruglyk, V.V. Osadchyi, Developing Competency in Programming among Future Software Engineers. Integration of Education 23(4), 587–606 (2019) 3. A.V. Spivakovskiy, N.A. Kushnir, N.V. Valko, M.A. Vinnyk, ICT Advanced Training of University Teachers. CEUR Workshop Proceedings 1844 (2017) 4. P.I. Fedoruk, Vykorystannya intelektualʹnykh ahentiv dlya intensyfikatsiyi protsesu navchannya (Use of intelligent agents to intensify the education process). Shtuchnyy intelekt 3, 379–384 (2004) 5. R.V. Streltsov, L.V. Slavinskaya, Iskusstvennyy intellekt v obrazovanii (Artificial intelligence in education). (DonNTU, Donetsk, 2010) 6. O.D. Humennyi, Kontseptsiya proektuvannya smartkompleksiv navchalʹnykh dystsyplin dlya zakladiv profesiynoyi (profesiyno-tekhnichnoyi) osvity (The concept of designing smart-complexes of educational disciplines for institutions of vocational (vocational-technical) education). Teoriya i metodyka profesiynoyi osvity 2 (18), 100–112 (2018) 7. V.V. Osadchyi, V.S. Kruglik, D.O. Bukreiev, Rozrobka prohramnoho zasobu dlya prohnozuvannya vstupu abituriyentiv do zakladiv vyshchoyi osvity (Development of a software tool for predicting admission of students to higher education institutions). Ukrainian Journal of Educational Studies and Information Technology 6(3), 55–69 (2018) 8. A.G. Molibog, Programmed training: Questions of the scientific organization of pedagogical work (1967) 9. A.M. Kuh, Dissertation, National Pedagogical Dragomanov University, 2018 10. P.D. Wasserman, Neural Computing: Theory and practice (1989) 11. D.A. Gubanov, D.A. Novikov, A.G. Chkhartishvili, Sotsial’nyye seti: modeli informatsionnogo vliyaniya, upravleniya i protivoborstva (Social networks: models of information influence, management and confrontation). (Publishing house of physical and mathematical literature, Moscow, 2010) 12. O. Markova, S. Semerikov, M. Popel, CoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. CEUR Workshop Proceedings 2104, 338–403 (2018), http://ceur-ws.org/Vol2104/paper_204.pdf. Accessed 30 Nov 2018 13. S.O. Semerikov, I.O. Teplytskyi, Yu.V. Yechkalo, A.E. Kiv, Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. CEUR Workshop Proceedings 2257, 122– 147 (2018), http://ceur-ws.org/Vol2257/paper14.pdf. Accessed 21 Mar 2019 14. S.O. Semerikov, I.O. Teplytskyi, Yu.V. Yechkalo, O.M. Markova, V.N. Soloviev, A.E. Kiv, Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. CEUR Workshop Proceedings 2393, 833–848 (2019), http://ceurws.org/Vol-2393/paper_348.pdf. Accessed 30 Jun 2019 15. S. Semerikov, I. Teplytskyi, Yu. Yechkalo, O. Markova, V. Soloviev, A. Kiv, Using spreadsheets as learning tools for computer simulation of neural networks. SHS Web of Conferences 75, 04018 (2020) 16. V. Osadchyi, N. Valko, L. Kuzmich, N. Abdullaeva, Studies of impact of specialized STEM training on choice further education. SHS Web of Conferences 75, 04014 (2020) 17. D. Bukreiev, Neuro-network technologies as a mean for creating individualization conditions for students learning. SHS Web of Conferences 75, 04013 (2020)

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