{"id":11363,"date":"2022-03-19T02:44:40","date_gmt":"2022-03-19T00:44:40","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=11363"},"modified":"2022-04-14T11:32:54","modified_gmt":"2022-04-14T08:32:54","slug":"intelligent-data-analysis-using-artificial-neural-networks-for-decision-making-in-the-education-domain","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=11363","title":{"rendered":"Intelligent data analysis using artificial neural networks  for decision making in the education domain"},"content":{"rendered":"<p><!--more--><\/p>\n<p style=\"text-align: center;\">INTELLIGENT DATA ANALYSIS USING ARTIFICIAL NEURAL NETWORKS FOR DECISION MAKING IN THE EDUCATION DOMAIN<\/p>\n<p style=\"text-align: center;\">\u0406\u041d\u0422\u0415\u041b\u0415\u041a\u0422\u0423\u0410\u041b\u042c\u041d\u0418\u0419 \u0410\u041d\u0410\u041b\u0406\u0417 \u0414\u0410\u041d\u0418\u0425 \u0406\u0417 \u0412\u0418\u041a\u041e\u0420\u0418\u0421\u0422\u0410\u041d\u041d\u042f\u041c \u0428\u0422\u0423\u0427\u041d\u0418\u0425 \u041d\u0415\u0419\u0420\u041e\u041d\u041d\u0418\u0425 \u041c\u0415\u0420\u0415\u0416 \u0414\u041b\u042f \u041f\u0420\u0418\u0419\u041d\u042f\u0422\u0422\u042f \u0420\u0406\u0428\u0415\u041d\u042c \u0423 \u0421\u0424\u0415\u0420\u0406 \u041e\u0421\u0412\u0406\u0422\u0418<\/p>\n<p><strong>\u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 111-114. \u041d\u043e\u043c\u0435\u0440: \u21166, 2021 (303)\u00a0<\/strong> <a href=\"http:\/\/journals.khnu.km.ua\/vestnik\/wp-content\/uploads\/2022\/03\/vknu-ts-2021-n6-303-111-114.pdf\"> <img loading=\"lazy\" class=\"size-full wp-image-69 alignnone\" src=\"http:\/\/journals.khnu.km.ua\/vestnik\/wp-content\/uploads\/2021\/01\/pdf.png\" alt=\"\" width=\"76\" height=\"32\" \/><\/a><br \/>\n<strong>\u0410\u0432\u0442\u043e\u0440\u0438<\/strong>:<br \/>\nPAVLO RADIUK<br \/>\nKhmelnytskyi National University<br \/>\nORCID ID: 0000-0003-3609-112X<br \/>\ne-mail: radiukpavlo@gmail.com<br \/>\nOLEXANDER MAZURETS<br \/>\nKhmelnytskyi National University<br \/>\nORCID ID: 0000-0002-8900-0650<br \/>\ne-mail: exe.chong@gmail.com<br \/>\nTETIANA SKRYPNYK<br \/>\nKhmelnytskyi National University<br \/>\nORCID ID: 0000-0002-8531-5348<br \/>\ne-mail: marine_1996@ukr.net<br \/>\nOLEKSANDR MOROZ<br \/>\nKhmelnytskyi National University<br \/>\nORCID ID: 0000-0003-4853-4573<br \/>\ne-mail: pointplack37@gmail.com<br \/>\n\u0420\u0410\u0414\u042e\u041a \u041f. \u041c., \u041c\u0410\u0417\u0423\u0420\u0415\u0426\u042c \u041e. \u0412., \u0421\u041a\u0420\u0418\u041f\u041d\u0418\u041a \u0422. \u041a., \u041c\u041e\u0420\u041e\u0417 \u041e. \u0412.<br \/>\n\u0425\u043c\u0435\u043b\u044c\u043d\u0438\u0446\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442<br \/>\n<strong>DOI:<\/strong> <a href=\"https:\/\/www.doi.org\/10.31891\/2307-5732-2021-303-6-111-114\">https:\/\/www.doi.org\/10.31891\/2307-5732-2021-303-6-111-114<\/a><br \/>\n<strong>\u0420\u0435\u0446\u0435\u043d\u0437\u0456\u044f\/Peer review : <\/strong>07.12.2021 \u0440.<br \/>\n<strong>\u041d\u0430\u0434\u0440\u0443\u043a\u043e\u0432\u0430\u043d\u0430<\/strong><strong>\/<\/strong><strong>Printed<\/strong><strong> :<\/strong> 30.12.2021 \u0440.<\/p>\n<p style=\"text-align: center;\"><strong>\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u043c\u043e\u0432\u043e\u044e \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u0443<\/strong><\/p>\n<p>Nowadays, applying educational intelligent data analysis (EIDA) seems relevant for improving the educational process based on big data. It implies developing and improving the methods of processing collected data in educational institutions to understand academic issues better. Over the past decades, artificial neural networks (ANNs) have been recognized as the most prominent techniques for learning analytics. In this work, we systematized the recent scientific literature in EIDA with ANNs. The paper analyzes the applications of ANN to EIDA and discusses the computational issues in the EIDA domain. According to the investigation, most educational data mining tasks are addressed by controlled learning models, such as classification, regression, and time-series prediction. Most in-depth methods used in the EIDA domain are traditional types of ANN. Well-known techniques such as multi-year perceptron and deep long short\u2010term memory networks have been mainly used for classification and prediction tasks within the education sphere. However, the difficulty of interpreting the results produced by ANNs has also been a challenge for intelligent data practitioners in any domain, including education.<br \/>\n<strong>Keywords:<\/strong> intelligent data analysis, data mining, artificial neural networks, deep learning, decision making, learning analytics, education<\/p>\n<p style=\"text-align: center;\"><strong>\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0443\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u043e\u044e \u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n<p>\u041d\u0430 \u0441\u044c\u043e\u0433\u043e\u0434\u043d\u0456 \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u043d\u043d\u044f \u043c\u0435\u0442\u043e\u0434\u0456\u0432 \u0456\u043d\u0442\u0435\u043b\u0435\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0430\u043d\u0430\u043b\u0456\u0437\u0443 \u0434\u0430\u043d\u0438\u0445 \u0443 \u0441\u0444\u0435\u0440\u0456 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\u0434\u043b\u044f \u0444\u0430\u0445\u0456\u0432\u0446\u0456\u0432-\u0430\u043d\u0430\u043b\u0456\u0442\u0438\u043a\u0456\u0432 \u0442\u0430 \u0440\u043e\u0437\u0440\u043e\u0431\u043d\u0438\u043a\u0456\u0432 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043d\u043e\u0433\u043e \u0437\u0430\u0431\u0435\u0437\u043f\u0435\u0447\u0435\u043d\u043d\u044f \u0432 \u0433\u0430\u043b\u0443\u0437\u0456 \u043e\u0441\u0432\u0456\u0442\u0438.<br \/>\n<strong>\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u0456\u043d\u0442\u0435\u043b\u0435\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u0438\u0439 \u0430\u043d\u0430\u043b\u0456\u0437 \u0434\u0430\u043d\u0438\u0445, \u0432\u0438\u0434\u043e\u0431\u0443\u0442\u043e\u043a \u0434\u0430\u043d\u0438\u0445, \u0448\u0442\u0443\u0447\u043d\u0456 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0456 \u043c\u0435\u0440\u0435\u0436\u0456, \u0433\u043b\u0438\u0431\u043e\u043a\u0435 \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f, \u043f\u0440\u0438\u0439\u043d\u044f\u0442\u0442\u044f \u0440\u0456\u0448\u0435\u043d\u044c, \u043e\u0441\u0432\u0456\u0442\u043d\u044f \u0430\u043d\u0430\u043b\u0456\u0442\u0438\u043a\u0430, \u0433\u0430\u043b\u0443\u0437\u044c \u043e\u0441\u0432\u0456\u0442\u0438.<\/p>\n<p style=\"text-align: center;\"><strong>References<\/strong><\/p>\n<ol>\n<li>Aldowah\u00a0H. Educational data mining and learning analytics for 21st century higher education: A review and synthesis \/ H.\u00a0Aldowah, H.\u00a0Al-Samarraie, W.\u00a0M.\u00a0Fauzy\u00a0\/\/ Telematics and Informatics. \u2013 2019. \u2013 Vol.\u00a037. \u2013 P.\u00a013\u201349.<\/li>\n<li>Salloum\u00a0S.\u00a0A. Mining in educational data: Review and future directions\u00a0\/ S.\u00a0A.\u00a0Salloum, M.\u00a0Alshurideh, A.\u00a0Elnagar, K.\u00a0Shaalan \/\/ Advances in Intelligent Systems and Computing. \u2013 Cham, 2020. \u2013 P.\u00a092\u2013102.<\/li>\n<li>Charitopoulos\u00a0A. On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010-2018 \/ A.\u00a0Charitopoulos, M.\u00a0Rangoussi, D.\u00a0Koulouriotis\u00a0\/\/ International Journal of Artificial Intelligence in Education. \u2013 2020. \u2013 Vol.\u00a030, no.\u00a03. \u2013 P.\u00a0371\u2013430.<\/li>\n<li>Rivas\u00a0A. Artificial neural network analysis of the academic performance of students in virtual learning environments \/ A.\u00a0Rivas [et al.] \/\/ Neurocomputing. \u2013 2021. \u2013 Vol.\u00a0423. \u2013 P.\u00a0713\u2013720.<\/li>\n<li>\u0420\u0430\u0434\u044e\u043a\u041f.\u00a0\u041c. \u0410\u043d\u0430\u043b\u0456\u0442\u0438\u0447\u043d\u0438\u0439 \u043e\u0433\u043b\u044f\u0434 \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440 \u0437\u0433\u043e\u0440\u0442\u043a\u043e\u0432\u0438\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436 \u0443 \u0437\u0430\u0434\u0430\u0447\u0430\u0445 \u0430\u043d\u0430\u043b\u0456\u0437\u0443 \u043c\u0435\u0434\u0438\u0447\u043d\u0438\u0445 \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c \/ \u041f.\u00a0\u041c. \u0420\u0430\u0434\u044e\u043a\u00a0 \/\/ Science, Engineering and Technology: Global and Current Trends : proceedings of inter. and pract. conf. (\u043c. \u041f\u0440\u0430\u0433\u0430, 27-28 \u0433\u0440\u0443\u0434. 2019). \u2013 \u041f\u0440\u0430\u0433\u0430, 2019. \u2013 \u0421. 32\u201325.<\/li>\n<li>Radiuk\u00a0P.\u00a0M. A framework for exploring and modelling neural architecture search methods. \/ P.\u00a0M.\u00a0Radiuk, N.\u00a0V.\u00a0Hrypynska \/\/ The 4th International Conference on Computational Linguistics and Intelligent Systems (COLINS-2020), Lviv, Ukraine, 23\u201324 April 2020. \u2013 CEUR-WS, 2020. \u2013 Vol. 2604. \u2013 P.\u00a01060\u20131074.<\/li>\n<li>Hassan S.-UI Virtual learning environment to predict withdrawal by leveraging deep learning\u00a0\/ S.-UlHassan [et al.] \/\/ International Journal of Intelligent Systems. \u2013 2019. \u2013 Vol.\u00a034, no.\u00a08. \u2013 P.\u00a01935\u20131952.<\/li>\n<li>Mubarak\u00a0A.\u00a0A. Prediction of students\u2019 early dropout based on their interaction logs in online learning environment\u00a0\/ A.\u00a0A.\u00a0Mubarak, H.\u00a0Cao, W.\u00a0Zhang\u00a0\/\/ Interactive Learning Environments. \u2013 2020. \u2013 P.\u00a01\u201320.<\/li>\n<li>Xu\u00a0W. Course video recommendation with multimodal information in online learning platforms: a deep learning framework\u00a0\/ Xu, Y.\u00a0Zhou\u00a0\/\/ British Journal of Educational Technology. \u2013 2020. \u2013 Vol.\u00a051, no.\u00a05. \u2013 P.\u00a01734\u20131747.<\/li>\n<li>Datta\u00a0D. Optimization of an automated examination generation system using hybrid recurrent neural network \/ D.\u00a0Datta, R.\u00a0Agarwal, I.\u00a0Tuteja \/\/ International Journal of Interdisciplinary Global Studies. \u2013 2020. \u2013 Vol.14, no. 4. \u2013 P.\u00a0246\u2013255.<\/li>\n<li>Nguyen\u00a0Ph.\u00a0X.\u00a0V. Deep learning versus traditional classifiers on vietnamese students\u2019 feedback corpus \/ Ph.\u00a0X.\u00a0V.\u00a0Nguyen, T. T. T. Hong, K. V. Nguyen, N. L.-T. Nguyen \/\/ 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City, 23\u201324 November 2018 \u2013 [S.\u00a0l.], 2018. \u2013 P.\u00a01\u20136.<\/li>\n<li>Mubarak\u00a0A.\u00a0A. Predictive learning analytics using deep learning model in MOOCs\u2019 courses videos \/ A.\u00a0A.\u00a0Mubarak, H.\u00a0Cao, S.\u00a0A.\u00a0M.\u00a0Ahmed\u00a0\/\/ Education and Information Technologies. \u2013 2021. \u2013 Vol. 26, no. 1. \u2013 P.\u00a0371\u2013392.<\/li>\n<li>Zaheer\u00a0N. Optimal school site selection in Urban areas using deep neural networks\u00a0\/ N.\u00a0Zaheer, -Ul Hassan, M.\u00a0Ali, M.\u00a0Shabbir\u00a0\/\/ Journal of Ambient Intelligence and Humanized Computing. \u2013 2021. \u2013 P.\u00a01\u201315.<\/li>\n<li>Furlan\u00a0R. A natural language processing\u2013based virtual patient simulator and intelligent tutoring system for the clinical diagnostic process: simulator development and case study\u00a0\/ R.\u00a0Furlan \/\/ JMIR medical informatics. \u2013 2021. \u2013 Vol.\u00a09, no.\u00a04. \u2013 P.\u00a0e24073.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[60],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/11363"}],"collection":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11363"}],"version-history":[{"count":2,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/11363\/revisions"}],"predecessor-version":[{"id":11938,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/11363\/revisions\/11938"}],"wp:attachment":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11363"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11363"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11363"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}