Artificial Neural Network in the CATS Training System
https://doi.org/10.38086/2522-9613-2019-2-53-59
Abstract
This paper presents a variant of using an artificial neural network (ANN) for adaptive learning. The main idea of using ANN is to apply it for a specific educational material, so that after completing the course or its separate topic, the student can determine, not only his level of knowledge, without the teacher’s participation, but also get some recommendations on what material needs to be studied further due to gaps in the studied issues. This approach allows you to build an individual learning trajectory, significantly reduce the time to study academic disciplines and improve the quality of the educational process. The training of an artificial neural network takes place according to the method of back propagation of an error. The developed ANN can be applied to study any academic discipline with a different number of topics and control questions. The research results are implemented and tested in the CATS adaptive training system. This system is the author's development.
About the Author
Yu. V. Popova
Belarusian National Technical University
Belarus
Belarus
Candidate of Science (Technology), Associate Professor
65 Independence Ave., 220013 Minsk, Republic of Belarus
References
1. Popova Ju.B. Classification of automated learning management systems // Sistemnyj analiz i prikladnaja informatika [System Analysis and Applied Informatics], 2008, 2, pp. 51–58 (in Russian).
2. Brusilovsky P. Adaptive and Intelligent Web-based Educational Systems // International Journal of Artificial Intelligence in Education, 2003, (2–4), pp. 159–172.
3. Brusilovskij P.L. Adaptive and intelligent technologies in network education // Novosti iskusstvennogo intellekta [Artificial Intelligence News], 2002, 5, pp. 25–31 (in Russian).
4. Brusilovskij P.L. Intelligent Tutoring Systems // Informatika. Informacionnye tehnologii. Sredstva i sistemy [Computer Science. Information Technology. Tools and Systems], 1990, 2, pp. 3–22 (in Russian).
5. Semenova N.G., Semenov A.M., Krylov I.B. Knowledge base of the intellectual training system of technical discipline // Vestnik Orenburgskogo gosudarstvennogo universiteta [Bulletin of the Orenburg State University], 2013, 9, pp. 44–54 (in Russian).
6. Peljushenko A.V. Learning environments and intelligent tutoring systems: opportunities for use in the educational process // Izvestija Volgogradskogo gosudarstvennogo tehnicheskogo universiteta [News of Volgograd State Technical University], 2006, 3, pp. 48–50 (in Russian).
7. Barla M., Bieliková M., Ezzeddinne A. B., Kramár T., Šimko M., Vozár O. On the impact of adaptive test question selection for learning efficiency // Computers & Education, 2010, 55 (2), pp. 846–857.
8. Grigor'ev A.P., Mamaev V.Ja. On the use of neural networks in knowledge testing // Nauchnoe priborostroenie [Scientific Instrument Engineering], 2016, 4, pp. 77–84 (in Russian).
9. Duplik S.V. Model of adaptive testing in fuzzy math // Informatika i obrazovanie [Computer science and Education], 2004, 11, pp. 57–65 (in Russian).
10. Kol'cov Ju. V., Dobrovol'skaja N. Ju. Neural network models in adaptive computer training // Educational Technology & Society, 2002, 5(2), pp. 213–216 (in Russian).
11. Stepanenko T.A. About adaptive learning in LMS Moodle // Matematicheskoe i programmnoe obespechenie sistem v promyshlennoj i social'noj sferah [Mathematical and Software Systems in the Industrial and Social Fields], Magnitogorsk, 2012, 2, pp. 371–372 (in Russian).
12. Popova Ju.B., Burakovskij A.I. Representation of knowledge in training systems based on the theory of fuzzy sets // Sistemnyj analiz i prikladnaja informatika [System Analysis and Applied Informatics], 2016, 2, pp. 58–65 (in Russian).
13. Karpenko A. P. Model provision of automated training systems. Overview // Nauka i obrazovanie [Science and Education], 2011. Available at: http://technomag.edu.ru/doc/193116.html (accessed: 09.11.2014) (in Russian).
14. Popova Ju.B. The functionality of CATS (Care About The Students) automated learning management system // Informatizacija obrazovanija i metodika jelektronnogo obuchenija [Informatization of Education and e-Learning Methods], Krasnojarsk, 2018, ch. 1, pp. 232–236 (in Russian).
15. David Kriesel. A Brief Introduction to Neural Networks. Available at: http://www.dkriesel.com/en/science/neural_networks (accessed: 17.09.2018).
16. Popova Ju.B., Jacynovich S.V. Training of artificial neural networks by the method of back propagation of error. Available at:http://www.bntu.by/news/67-conference-mido/4860-2016-11-18-15-47-40.html (accessed: 17.09.2018) (in Russian).
17. Popova Ju.B., Jacynovich S.V. Implementation of an artificial neural network for managing virtual objects // Sistemnyj analiz i prikladnaja informatika [System Analysis and Applied Informatics], 2017, 4, pp. 72–78 (in Russian).
Review
For citations:
Popova Yu.V. Artificial Neural Network in the CATS Training System. Digital Transformation. 2019;(2):53-59. (In Russ.) https://doi.org/10.38086/2522-9613-2019-2-53-59
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