Abstract
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.
Publisher
Academy of Cognitive and Natural Sciences
Reference39 articles.
1. 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: https://doi.org/10.1109/ICSIMA.2013.6717966.
2. 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: http://dspace.tnpu.edu.ua/jspui/bitstream/123456789/16319/1/Conf_Shlax_nayky.pdf#page=110.
3. 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: https://doi.org/10.1016/j.econedurev.2015.02.003.
4. 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: http://eprints.mdpu.org.ua/id/eprint/8136/.
5. 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: https://doi.org/10.1007/978-3-030-52240-7_7.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献