Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach




evaluation criteria, educational program, educational activities, prognostication, rating, ANFIS, artificial neural networks


In the process of self-assessment and accreditation examination, assessment is carried out according to a scale that covers four levels of compliance with the quality criteria of the educational program and educational activities. Assessing the quality of education is complicated by the fact that the value of quality criteria is due to a large number of factors, possibly with an unknown nature of influence, as well as the fact that when conducting pedagogical measurements it is necessary to work with non-numerical information. To solve these problems, the authors proposed a method for assessing the quality of educational programs and educational activities based on the adaptive neuro-fuzzy input system (ANFIS), implemented in the package Fuzzy Logic Toolbox system MATLAB and artificial neural network direct propagation with one output and multiple inputs. As input variables of the system ANFIS used criteria for evaluating the educational program. The initial variable of the system formed a total indicator of the quality of the curriculum and educational activities according to a certain criterion or group of criteria. The article considers a neural network that can provide a forecast for assessing the quality of educational programs and educational activities by experts. The training of the artificial neural network was carried out based on survey data of students and graduates of higher education institutions. Before the accreditation examination, students were offered questionnaires with a proposal to assess the quality of the educational program and educational activities of the specialty on an assessment scale covering four levels. Student assessments were used to form the vector of artificial neural network inputs. It was assumed that if the assessments of students and graduates are sorted by increasing the rating based on determining the average grade point average, the artificial neural network, which was taught based on this organized data set, can provide effective forecasts of accreditation examinations. As a result of comparing the initial data of the neural network with the estimates of experts, it was found that the neural network does make predictions quite close to reality.


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Arsad, P.M., Buniyamin, N. and Manan, J.l.A., 2013. A neural network students’ performance prediction model (nnsppm). 2013 ieee international conference on smart instrumentation, measurement and applications (icsima). pp.1–5. Available from: DOI:

Belyux, K.V. and Sitkar, T.V., 2020. Vykorystannya glybynnyx nejronnyh merezh v naukovyh doslidzhennyah ta osviti [The use of deep neural networks in research and education]. Shlyax u nauku: pershi kroky. pp.379–384. Available from:

Black, S.E., Lincove, J., Cullinane, J. and Veron, R., 2015. Can you leave high school behind? Economics of education review, 46, pp.52–63. Available from: DOI:

Bukreyev, D.O. and Serdyuk, I.M., 2019. Metod vykorystannya nejronnoyi merezhi dlya prognozuvannya efektyvnosti roboty studentiv [A method for using a neural network to predict student performance]. Informacijni texnologiyi v osviti ta nauci. vol. 11, pp.61–64. Available from:

Cader, A., 2020. The potential for the use of deep neural networks in e-learning student evaluation with new data augmentation method. In: I.I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin and E. Millán, eds. Artificial intelligence in education. Cham: Springer International Publishing, pp.37–42. Available from: DOI:

Chaban, H. and Kukhtiak, O., 2020. Zastosuvannya aparata teoriyi shtuchnyx nejronnyx merezh u zadachax prykladnoyi pedagogiky zakladiv vyshhoyi osvity [Application of the artificial neural networks theory in problems of applied pedagogy of higher education institutions]. Ukrainian educational journal, 1, pp.51–56. Available from: DOI:

Cherniak, O., Sorocolat, N. and Kanytska, I., 2020. Graph analytical method for determining the complex quality indicator of qualimetry objects. Innovative technologies and scientific solutions for industries, 4(14), pp.169–175. Available from: DOI:

Chervak, O.Y., 2010. Teoriya optymalnogo vyboru. Pidkryteriyi paretivskoyi zgortky kryteriyiv [The theory of optimal choice. Sub-criteria for convolution of Pareto criteria]. Naukovyj visnyk uzhgorodskogo universytetu, 30, pp.28–30. Available from:

Do, Q.H. and Chen, J.F., 2013. A neuro-fuzzy approach in the classification of students’ academic performance. Computational intelligence and neuroscience, 2013, p.179097. Available from: DOI:

Fazlollahtabar, H. and Mahdavi, I., 2009. User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach. Educational research review, 4(2), pp.142–155. Available from: DOI:

Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning, Adaptive Computation and Machine Learning series. MIT Press. Available from:

Grycyuk, Y.I. and Grycyuk, M.Y., 2014. Osoblyvosti multyplikatyvnogo zgortannya chastkovyh kryteriyiv v uzagalnenyj pokaznyk [Features of multiplicative folding of partial criteria into the generalized indicator]. Naukovyj visnyk nltu ukrayiny, 24(11), pp.341–352. Available from:

Hammerness, K. and Klette, K., 2015. Indicators of quality in teacher education: Looking at features of teacher education from an international perspective. Promoting and sustaining a quality teacher workforce. Emerald Group Publishing Limited, International Perspectives on Education and Society, vol. 27, pp.239–277. Available from: DOI:

Horal, L., Khvostina, I., Reznik, N., Shiyko, V., Yashcheritsyna, N., Korol, S. and Zaselskiy, V., 2020. Predicting the economic efficiency of the business model of an industrial enterprise using machine learning methods. Ceur workshop proceedings, 2713, pp.334–351. DOI:

Isac, C., Nita, D. and Dura, C., 2010. Optimizing franchising investment decision using Electre and Rompedet methods. The iup journal of managerial economics, 8(1/2), pp.7–32.

Kardan, A.A., Sadeghi, H., Ghidary, S.S. and Sani, M.R.F., 2013. Prediction of student course selection in online higher education institutes using neural network. Computers & education, 65, pp.1–11. Available from: DOI:

Kirichek, G., Harkusha, V., Timenko, A. and Kulykovska, N., 2019. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. Ceur workshop proceedings, 2546, pp.138–148. DOI:

Kondruk, N.E. and Malyar, M.M., 2019. Bagatokryterialna optymizaciya linijnyh system [Multicriteria optimization of linear systems]. Uzhgorod, Ukraine: RA AUTDOR-ShARK. Available from:

Lesinski, G., Corns, S. and Dagli, C., 2016. Application of an artificial neural network to predict graduation success at the united states military academy. Procedia computer science, 95, pp.375–382. Available from: DOI:

Liu, C., Feng, Y. and Wang, Y., 2022. An innovative evaluation method for undergraduate education: an approach based on bp neural network and stress testing. Studies in higher education, 47(1), pp.212–228. Available from: DOI:

Mahapatra, S.S. and Khan, M.S., 2007. A neural network approach for assessing quality in technical education: an empirical study. International journal of productivity and quality management, 2(3), pp.287–306. Available from: DOI:

Markova, O., Semerikov, S. and Popel, M., 2018. CoCalc as a learning tool for neural network simulation in the special course “Foundations of mathematic informatics”. Ceur workshop proceedings, 2104, pp.388–403. Available from: DOI:

Müller, B., Reinhardt, J. and Strickland, M.T., 1995. Neural Networks: An Introduction, Physics of Neural Networks. 2nd ed. Springer-Verlag Berlin Heidelberg. Available from: DOI:

Naser, S.A., Zaqout, I., Ghosh, M.A., Atallah, R. and Alajrami, E., 2015. Predicting student performance using artificial neural network: In the faculty of engineering and information technology. International journal of hybrid information technology, 8(2), pp.221–228. Available from: DOI:

Okubo, F., Yamashita, T., Shimada, A. and Ogata, H., 2017. A neural network approach for students’ performance prediction. Proceedings of the seventh international learning analytics & knowledge conference. New York, NY, USA: Association for Computing Machinery, LAK ’17, p.598–599. Available from: DOI:

Osadchyi, V., Kruglyk, V. and Bukreyev, D., 2018. Development of a software product for forecasting the entrance of applicants to higher educational institutions. Ukrainian journal of educational studies and information technology, 6(3), pp.55–69. Available from: DOI:

Pererva, V.V., Lavrentieva, O.O., Lakomova, O.I., Zavalniuk, O.S. and Tolmachev, S.T., 2020. The technique of the use of Virtual Learning Environment in the process of organizing the future teachers’ terminological work by specialty. Ceur workshop proceedings, 2643, pp.321–346. DOI:

Porokhnya, V. and Ostapenko, O., 2019. Neural network and index forecasting of the strategies of development of the armed forces of Ukraine depending on their own economic opportunities and encroachments of the aggressor states. Ceur workshop proceedings, 2422, pp.111–120. DOI:

Pârvu, I. and Ipate, D.M., 2007. Mathematical model of measuring the quality of services of the higher education institutions. Journal of applied economic sciences, 2(1(2)_Fall2007). Available from:

Regulations on the accreditation of educational programs, which provide training for higher education, 2019. Available from:

Rivas, A., González-Briones, A., Hernández, G., Prieto, J. and Chamoso, P., 2021. Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing, 423, pp.713–720. Available from: DOI:

Shtovba, S.D., 2007. Proektirovanie nechetkikh sistem sredstvami MatLab [Fuzzy systems design of by means of MatLab]. Moscow: Goryachaya Liniya–Telekom. Available from:

Shtovba, S.D. and Pankevych, O.D., 2018. Fuzzy technology-based cause detection of structural cracks of stone buildings. Ceur workshop proceedings, 2105, pp.209–218.

Sivanandam, S.N., Sumathi, S. and Deepa, S.N., 2006. Introduction to neural networks using matlab 6.0. New Delhi: Tata McGraw-Hill Education.

Tarasenko, A.O., Yakimov, Y.V. and Soloviev, V.N., 2019. Convolutional neural networks for image classification. Ceur workshop proceedings, 2546, pp.101–114. DOI:

Taylan, O. and Karagözoğlu, B., 2009. An adaptive neuro-fuzzy model for prediction of student’s academic performance. Computers & industrial engineering, 57(3), pp.732–741. Available from: DOI:

Valko, N. and Osadchyi, V., 2020. Education individualization by means of artificial neural networks. E3s web of conferences, 166, p.10021. Available from: DOI:

Waheed, H., Hassan, S.U., Aljohani, N.R., Hardman, J., Alelyani, S. and Nawaz, R., 2020. Predicting academic performance of students from vle big data using deep learning models. Computers in human behavior, 104, p.106189. Available from: DOI:

Wächter, B., Kelo, M., Lam, Q., Effertz, P., Jost, C. and Kottowski, S., 2015. University quality indicators: a critical assessment. Directorate General for Internal Policies, Policy Department B: Structural and Cohesion Policies. Available from:





How to Cite

Ryabko, A.V., Zaika, O.V., Kukharchuk, R.P., Vakaliuk, T.A. and Osadchyi, V.V., 2022. Methods for predicting the assessment of the quality of educational programs and educational activities using a neuro-fuzzy approach. CTE Workshop Proceedings [Online], 9, pp.154–169. Available from: [Accessed 20 March 2023].



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