kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-3-49-58kaz29-2101Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEНЕЙРОСЕТЕВЫЕ АЛГОРИТМЫ ДЛЯ ИНТЕЛЛЕКТУАЛЬНОЙ ОБРАБОТКИ ОТЗЫВОВ СТУДЕНТОВNEURAL NETWORK ALGORITHMS FOR INTELLIGENT PROCESSING OF STUDENTS’ REVIEWShttps://orcid.org/0009-0001-2233-092XАртыкбаеваА. А.ArtykbayevaA. A.

докторант

г. Костанай

 PhD student 

 Kostanay 

Asel_aidarbekowna@mail.ruhttps://orcid.org/0000-0002-8681-4552СалыковаО. С.SalykovaO. S.

к.т.н., ассоциированный профессор

 г. Костанай 

Cand. Tech. Sc., Associate Professor

Kostanay

Solga0603@mail.ruhttps://orcid.org/0009-0002-4459-5010НурмагамбетоваЛ. И.NurmagambetovaL. I.

к.э.н., ассоциированный профессор

г. Костанай

Cand. Econ. Sc., Associate Professor

Kostanay 

Leila0205@mail.ruКостанайский региональный университет им. Ахмета БайтурсыноваКазахстанKostanay Regional University named after Akhmet BaitursynovKazakhstanКостанайский инженерно-экономический университет им. Мыржакыпа ДулатоваКазахстанKostanay Engineering and Economics University named after M. DulatovKazakhstan2025270920252234958Copyright © Артыкбаева А.А., Салыкова О.С., Нурмагамбетова Л.И., 20252025Артыкбаева А.А., Салыкова О.С., Нурмагамбетова Л.И.Artykbayeva A.A., Salykova O.S., Nurmagambetova L.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2101

Данная статья посвящена проблеме использования нейросетевых алгоритмов для автоматизированного анализа отзывов студентов. В современных условиях многопрофильных учебных заведений и платформ онлайн-обучения успеваемость студентов становится важным показателем качества образовательного процесса и служит основой для дальнейших корректировок. Классические подходы, такие как ручная обработка и описательная статистика, не всегда способны ответить на вопрос, насколько глубоко можно понять и проанализировать мнения студентов. К нейросетевым алгоритмам, в сравнении с традиционными методами обработки текста, относятся рекуррентные нейронные сети (RNN), BERT и трансформаторы, которые имеют больший объем текстовой информации и могут использовать более эффективные логические подходы к изучению скрытых закономерностей. В статье рассматриваются подходы к обработке и анализу отзывов, этапы разработки нейросетевых алгоритмов и их возможное влияние на образование. Обсуждается потенциал более продвинутых нейросетевых методов, включая метод обучения на большом объеме данных, контекстное понимание, а также меньшее количество единиц данных. Исследование нейросетевого подхода также свидетельствует о том, что важно уделять внимание этике и объяснению. В статье в последующих частях сделан вывод о том, что использование нейросетевых алгоритмов способствует оптимизации управления образовательными курсами и повышению уровня их востребованности среди студентов, а также ставится вопрос о дальнейших исследованиях данной темы.

This article is devoted to the problem of using neural network algorithms for automated analysis of student reviews. In the modern conditions of multidisciplinary educational institutions and online learning platforms, student performance becomes an important indicator of the quality of the educational process and serves as a basis for further adjustments. Classical approaches, such as manual processing and descriptive statistics, are not always able to answer the question of how deeply students’ opinions can be understood and analyzed. Neural network algorithms, in comparison with traditional text processing methods, include Recurrent Neural Networks (RNN), BERT and transformers, which have a larger volume of text information and can use more effective logical approaches to the study of hidden patterns. The article considers approaches to processing and analyzing reviews, stages of developing neural network algorithms and their possible impact on education. The potential of more advanced neural network methods is discussed, including a method of learning on a large amount of data, contextual understanding, as well as a smaller number of data units. The study of the neural network approach also indicates that it is important to pay attention to ethics and explanation. The article, in subsequent parts, came to the conclusion that the use of neural network algorithms helps to optimize the management of educational courses and increase the level of their demand among students and raises the question of further research on this topic.

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The authors declare that there are no conflicts of interest present.