Application of machine learning methods to analysis and evaluation of distance education

Abstract

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.