FORECASTING THE RESULTS OF THE SINGLE ENTRANCE EXAM IN A FOREIGN LANGUAGE BY BACHELORS OF THE INSTITUTION OF HIGHER EDUCATION
The paper provides information on the need to pass the “Unified entrance exam” in a foreign (English) language by students who have received a bachelor’s degree and wish to continue their studies to obtain a master’s degree. It is determined that when working with undergraduate students, it is advisable, firstly, to determine the percentage of graduates whose passing EVE is unlikely, and secondly, to intensify work with such graduates to increase this probability. The task was set to create a model for predicting the results of the unified entrance exam in a foreign language by bachelor’s graduates of higher education institutions upon entering the master’s program. A number of factors that affect the EVE score are proposed, namely: competitive score at enrollment (indicator of the student’s basic level), rating (assessment) based on the results of the first year of study (exam in the compulsory subject “Foreign Language”), choice “Foreign language” in the 2-3rd year (maximum of all or “0”, if the student did not choose), the rating of additional classes “Foreign language” in the 4th year, the average rating for the penultimate session (indicator “current” student’s attitude to the educational process), the fact of having additional points (an indicator of the student’s interest in other activities than learning), the average rating of a bachelor’s degree (an indicator of the general student’s attitude to the educational process). The available data concerning students of two years of the department of intelligent decision-making systems of the Donbas State Engineering Academy are given. A method of artificial neural networks with a two-layer perceptron architecture with ten neurons in each hidden layer, a sigmoid activation function, and an error backpropagation algorithm for network training is proposed. Calculations were performed in the Deductor Studio Lite environment, their results were analyzed. It is noted that the proposed approach to forecasting can be applied when working with undergraduate students, to determine the percentage of graduates whose EVI is unlikely to pass, and to intensify work with such graduates to increase this probability.
Keywords: educational and qualification level, the only entrance exam, forecasting, artificial neural network, perceptron, sigmoid, network training.
Nakaz Ministerstva osvity` i nauky` Ukrayiny` vid 15 zhovtnya 2020 r. # 1274 «Pro zatverdzhennya umov pry`jomu na navchannya dlya zdobuttya vy`shhoyi osvity` v 2021 roci». [Order of the Ministry of Education and Science of Ukraine of October 15, 2020 № 1274 "On approval of the conditions of admission to higher education in 2021."] Available at: https://mon.gov.ua/ua/npa/pro-zatverdzhennya-umovprijomu-na-navchannya-dlya-zdobuttya-vishoyi-osviti-v-2021-roci (accessed 16.03.2021).
Makhmutova L. R. Faktory vliyaniya na uspevaemost' studentov v vuze [Factors influencing student performance at the university]. Organization of work with youth. 2018. № 1. URL: http://www.es.rae.ru/ovv/282-1208 (accessed 16.03.2021).
Blokhina M. V., Vakhitov Sh. M., Sytnik V. V. Analiz i otsenka akademicheskoy uspevaemosti studentov vuzov – odna iz funktsiy pedagogicheskogo menedzhmenta [Analysis and assessment of the academic performance of university students is one of the functions of pedagogical management]. Advances in modern natural science. 2008. № 2. PP. 52–54.
Shmarikhina E. S. Issledovanie faktorov uspevaemosti obuchayushchikhsya [Study of the factors of student achievement]. NSUE Bulletin. 2018. №3. URL: https://cyberleninka.ru/article/n/issledovanie-faktorov-uspevaemostiobuchayuschihsya (accessed 16.04.2020).
Proshkina E. N., Balashova I. Yu. Analiz i prognozirovanie uspevaemosti studentov na osnove radial'noy bazisnoy neyronnoy seti [Analysis and prediction of student performance based on a radial basic neural network]. Technical sciences: traditions and innovations: materials of the III Intern. scientific. conf. (Kazan, March 2018). Kazan: Young Scientist, 2018, pp. 24–28. URL: https://moluch.ru/conf/tech/archive/287/13683/ (accessed 16.03.2021: 16.04.2020).
Kallan R. Osnovnye kontseptsii neyronnykh setey [Basic concepts of neural networks]. Moscow: Williams Publ., 2001. 288 p.
Khaykin S. Neyronnye seti: polnyy kurs, 2-e izdanie [Neural Networks: Complete Course, 2nd Edition] / Translation from English. Moscow: Williams Publ., 2006. 1104 p.
Kovalevskiy S. V., Gitis V. B. Sozdanie i primenenie neyronnykh setey dlya resheniya prikladnykh zadach: Uchebno-metodicheskoe posobie dlya studentov spetsial’nosti «Intellektual’nye sistemy prinyatiya resheniy» [Creation and application of neural networks for solving applied problems: Study guide for students of the specialty "Intelligent decision-making systems"]. Kramatorsk: DSEA, 2008. 75 p.
BaseGroup Labs: ofitsial’nyy sayt [BaseGroup Labs: official site]. Available at: https://basegroup.ru/community/articles/intro (accessed 16.03.2021).
Mel’nikov A. Yu., Shevchenko N. Yu. Modelirovanie vliyaniya NIRS na rezul’taty itogovoy attestatsii studentov [Modeling the influence of research work on the results of the final certification of students]. Informatics, management and artificial intelligence. Abstracts of the seventh international scientific and technical conference. Kharkiv: NTU "KhPI", 2020, p. 52.
Mel`ny`kov O. Yu., Shevchenko N. Yu. Zastosuvannya nejronny`x merezh dlya prognozuvannya rezul`tativ pidsumkovoyi atestaciyi studentiv zakladu vy`shhoyi osvity` v zalezhnosti vid efekty`vnosti yix naukovo-doslidnoyi roboty` [The use of neural networks to predict the results of the final certification of students of higher education depending on the effectiveness of their research work]. Neural network technologies and their application NMTiZ-2020: collection of scientific works of the XIX International scientific conference "Neural network technologies and their application NMTiZ-2020" / for general. ed. SV Kovalevsky. Kramatorsk: DSEA, 2020, pp. 111–115.
Melnykov A.Yu., Shevchenko N.Yu., Isakova Ye.P., Bobkova E.Yu. Modeling the impact of University students research work on the results of their final certificationю J. Phys.: Conf. Ser., 2020, Vol. 1691, 012187. Available at: https://doi.org/10.1088/1742- 6596/1691/1/012187 (accessed 16.03.2021).
How to Cite
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).