International Journal of Electrical and Computer Engineering (IJECE) Vol. 15, No. 2, April 2025, pp. 2172~2180 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i2.pp2172-2180  2172 Journal homepage: http://ijece.iaescore.com Application of machine learning methods to analysis and evaluation of distance education Ainur Mukhiyadin 1 , Manargul Mukasheva 2 , Ulzhan Makhazhanova 1 , Aislu Kassekeyeva 1 , Gulmira Azieva 1 , Zhanat Kenzhebayeva 3 , Alfiya Abdrakhmanova 1 1 Department of Information systems, Eurasian National University named L.N.Gumilyov, Astana, Republic of Kazakhstan 2 Digital Learning Research Laboratory, National Academy of Education named after I. Altynsarin, Astana, Republic of Kazakhstan 3 Department of Computer Science at the Caspian University of Technology and Engineering, named after Sh. Yessenov, Aktau, Republic of Kazakhstan Article Info ABSTRACT Article history: Received Aug 13, 2024 Revised Oct 26, 2024 Accepted Nov 20, 2024 In recent decades, distance learning has become an essential component of the modern educational system, providing students with flexibility and access to knowledge regardless of location. This paper discusses creating a hybrid machine-learning model for assessing the quality of distance learning based on survey data. The model combines two feature extraction methods: Term frequency-inverse document frequency (TF-IDF) and Word2Vec. Combining these methods allows for a more complete and accurate representation of text data, improving the quality of machine learning models. The study aims to develop and evaluate the effectiveness of the proposed hybrid model for analyzing survey data and assessing the quality of distance learning. The paper considers the tasks of collecting and preprocessing text data, experimentally comparing various feature extraction methods and their combinations, training and evaluating a machine learning model based on a combination of TF-IDF and Word2Vec features, as well as analyzing the results and assessing the effectiveness of the proposed model using various metrics. In conclusion, the prospects for further development and application of the proposed model in educational institutions to improve the quality of distance learning are discussed. Keywords: Distance learning Machine learning Quality assessment Term frequency-inverse document frequency Text data analysis Word2Vec This is an open access article under the CC BY-SA license. Corresponding Author: Manargul Mukasheva Digital Learning Research Laboratory, National Academy of Education named after I. Altynsarin 010000 Astana, Republic of Kazakhstan Email: manargul.mukasheva@mail.ru 1. INTRODUCTION In recent decades, distance learning [1]–[3] has become an essential component of the modern educational system [4], providing students with flexibility and access to knowledge regardless of location. The development of technology and the internet has significantly expanded the possibilities of distance learning [5]–[7], making it available to millions of students worldwide. At the same time, the growth in distance learning programs has led to the need to develop methods for assessing their quality, which is essential for ensuring effective learning and student satisfaction. Traditional methods of evaluating the quality of education [8], such as questionnaires and surveys, often do not provide a complete and objective picture. Modern approaches to data analysis based on machine learning and natural language processing methods [9]–[11] offer new opportunities for more accurate and detailed analysis of text data obtained from student and teacher surveys. One promising area is the use of hybrid models that combine various feature extraction methods to improve the quality of analysis. This article discusses creating a hybrid machine-