{"id":2179,"date":"2025-05-13T11:41:12","date_gmt":"2025-05-13T10:41:12","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2179"},"modified":"2026-06-04T13:40:18","modified_gmt":"2026-06-04T12:40:18","slug":"26-5-8-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-5-8-en\/","title":{"rendered":"26.5.8 en"},"content":{"rendered":"\n

Main directions of data mining in the field of education<\/strong><\/h2>\n\n\n\n

N.A. Popova, E.S. Egorova<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract. <\/em><\/strong>Data mining in education is becoming increasingly popular and many educational institutions are increasingly applying it to improve their competitiveness. Many studies have been conducted recently on educational data analysis on different educational topics with the use of different methods and algorithms. Therefore, it would be useful to have a brief overview of the most used methods and approaches. For this purpose, foreign and domestic works were analyzed to identify the most relevant research directions, important methods and algorithms in the field of educational data analysis in modern higher education. A systematic analysis methodology consisting of 5 stages was proposed to compile the review. Widely used topics, methods, algorithms were identified and the relationship between them was established. The scientific novelty of the overview lies in identifying the current research challenges in the field of educational data analysis in higher education and discovering promising research methods and algorithms.<\/p>\n\n\n\n

Keywords: <\/em><\/strong>Data Mining, educational data mining, meta-analysis, business intelligence<\/p>\n\n\n\n

For  citation<\/strong>.  Popova N.A.,  Egorova E.S. <\/sup>Main  directions  of  data  mining  in  the  field  of  education. News  of  the  Kabardino-Balkarian  Scientific  Center  of  RAS.<\/em> <\/strong>2024.  Vol. 26.  No. 5.  Pp. 94\u2013106. DOI: 10.35330\/ <\/strong>1991-6639-2024-26-5-94-106<\/p>\n\n\n\n

References<\/strong><\/summary>\n
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    <\/p>\n<\/details>\n\n\n\n

    Information about the authors<\/strong><\/summary>\n

    Nataliya A. Popova<\/strong>, Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Mathematical Support and Computer Use, Penza State University;
    440026, Russia, Penza, 40 Krasnaya street;
    popov.tasha@yandex.ru, ORCID: https:\/\/orcid.org\/0000-0001-9713-4897, SPIN-\u043a\u043e\u0434: 9358-8567
    Ekaterina S. Egorova<\/strong>, Candidate of Economic Sciences, Associate Professor of the Department of Applied Informatics, Penza State Technological University;
    440039, Russia, Penza, 1a\/11 Baidukova Passage\/Gagarina street;
    katepost@yandex.ru, ORCID: https:\/\/orcid.org\/0000-0002-0816-0944, SPIN-\u043a\u043e\u0434: 5624-6036<\/p>\n\n\n\n

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    Main directions of data mining in the field of education N.A. Popova, E.S. Egorova Abstract. Data mining in education is becoming increasingly popular and many educational institutions are increasingly applying it to improve their competitiveness. Many studies have been conducted recently on educational data analysis on different educational topics with the use of different methods […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2179","page","type-page","status-publish","hentry"],"yoast_head":"\n26.5.8 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-5-8-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"26.5.8 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Main directions of data mining in the field of education N.A. Popova, E.S. Egorova Abstract. Data mining in education is becoming increasingly popular and many educational institutions are increasingly applying it to improve their competitiveness. 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