QUALITY MANAGEMENT OF THE EDUCATIONAL PROCESS ON THE BASIS OF THE ANALYSIS OF TEST TASKS BY USING ARTIFICIAL INTELLIGENCE

Introduction. The use of information and communication technologies allows the teacher to update the content of training. The success of the educational process is largely determined by the ICT competence and ICT literacy of the teacher. Data mining based on artificial neural networks can be used as one of the elements of information and communication technologies. The main direction of application of tests is to measure the level of knowledge of students. A large amount of accumulated test results with proper processing can provide the teacher with additional (hidden) data. The use of an artificial neural network for the analysis of test results makes it possible to expand the direction of the tests. Methods. The theoretical basis of the study is based on a complex of scientific statements of domestic and foreign scientists in the field of education and artificial intelligence. Practical research methods are based on an experiment in creating a test in computer science, testing students and accumulating data, as well as their processing simulated artificial neural network. Results. The article describes the process of analyzing the results of research using the SPSS STATISTICA 20 programs. The search for hidden patterns of the test was carried out, the reliability of the results obtained was verified. Discussion. There was suggested the possibility of further application of the results obtained in the educational process. The results obtained can be used to search for and correct difficult or easy test items, and to replace failed test tasks. The teacher has the opportunity to redistribute the time resource for the study of difficult digestible topics, due to easily digestible. The idea of an integrated approach to the use of artificial neural networks in education is proposed.

1. Gorjushkin E.I. Ispol'zovanie nejrosetevyh tehnologij v teorii adaptivnogo testirovanija po informatike v vuze. Diss. kand.ped.nauk. Moscow, 2009, p. 174.

2. Gorjushkin E.I., Chistjakov M.V. Rol' informacionno-kommunikacionnyh tehnologij i IKT-kompetentnosti prepodavatelja v processe obuchenija medicinskoj informatike i informatike. Kursk, 2016, vol. 2, pp. 417-419.

3. Zaharova I.G., Plotonenko Ju.A., Tarasova O.V. Prognozirovanie kachestva vysshego obrazovanija na osnove tehnologij mashinnogo obuchenija. Informatizacija nepreryvnogo obrazovanija – 2018. Moscow, RUDN Publ., 2018, vol.1, pp. 60-64.

4. Modul' IBM SPSS Neural Networks. URL: http://www.predictive.ru/software/neural_networks.htm

5. Nesterova I.A. Informacionno-kommunikativnye tehnologii. Obrazovatel'naja jenciklopedija ODiplom.ru. URL: http://odiplom.ru/lab/informacionno-kommunikativnye-tehnologii.html (data obrashhenija: 03.09.2018).

6. Orazgalieva M.D. Informacionnokommunikativnye tehnologii v obrazovatel'nom processe. URL: https://infourok.ru/informacionnokommunikativnie-tehnologiiv-obrazovatelnom-processe-545531.html (data obrashhenija: 03.09.2018)

7. Pavljuk A.A. Sistema intellektual'nogo analiza dannyh dlja prognozirovanija uspeshnosti uchebnoj dejatel'nosti. Diss. kand.ped.nauk. Moscow, 2005, 116 p.

8. Urman A. Nejroset' vmesto prepodavatelja, igra vmesto zubrezhki: kak iskusstvennyj intellekt izmenit obrazovanie. URL: https://knife.media/ai-education/ (data ob-rashhenija: 02.12.2018)

9. Usachev A.V. Nejrosetevaja mul'tilingvisticheskaja sistema adaptivnogo obuchenija terminologicheskoj leksike. Diss. kand.ped.nauk. Moscow, 2005, 158 p.

10. Chubukova I.A. Data Mining. Moscow, 2016, 471 p.

11. Daniel J Levitin Q&A. Why the modern world is bad for your brain. URL: https://www.theguardian.com/science/2015/jan/18/modern-world-bad-for-brain-daniel-jlevitin-organized-mind-informationoverload (data obrashhenija: 04.10.2018)