[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121224-en":3,"doc-seo-121224-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121224,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of Machine Learning Methods to Analysis and Evaluation of Distance Education - Hybrid Model with TF-IDF and Word2Vec","Distance learning has become central to modern education, yet assessing program quality remains challenging because traditional surveys may lack completeness and objectivity. This paper develops and evaluates a hybrid machine-learning approach for quality assessment using survey text data. The model integrates two feature extraction methods—TF-IDF and Word2Vec—to capture both key terms and semantic meaning. The study covers text collection and preprocessing, experimental comparison of feature representations, model training and evaluation using multiple metrics, and analysis of results to verify the effectiveness of the proposed hybrid design.","Application of machine learning methods to analysis and evaluation of distance education  \nAinur Mukhiyadin1, Manargul Mukasheva2, Ulzhan Makhazhanova1, Aislu Kassekeyeva1, Gulmira Azieva1, Zhanat Kenzhebayeva3, Alfiya Abdrakhmanova1  \n1Department of Information systems, Eurasian National University named L.N.Gumilyov, Astana, Republic of Kazakhstan 2Digital Learning Research Laboratory, National Academy of Education named after I. Altynsarin, Astana, Republic of Kazakhstan 3Department of Computer Science at the Caspian University of Technology and Engineering, named after Sh. Yessenov, Aktau,  \nRepublic of Kazakhstan  \nArticle history:  \nReceived Aug 13, 2024 Revised Oct 26, 2024 Accepted Nov 20, 2024  \nKeywords:  \nDistance learning Machine learning Quality assessment Term frequency-inverse document frequency Text data analysis Word2Vec  \nCorresponding Author:  \nIn 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 ofTF-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.  \nThis is an open access article under the CC BY-SA license.  \nManargul Mukasheva  \nDigital Learning Research Laboratory, National Academy of Education named after I. Altynsarin 010000 Astana, Republic of Kazakhstan  \n[Email: manargul.mukasheva@mail.ru](Email: manargul.mukasheva@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn 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-  \nlearning 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. TF-IDF allows you to highlight essential terms in the text, while Word2Vec represents words as dense vectors that reflect their semantic meaning. Combini","cbCaioWwUHpBs7oO","https://ap.wps.com/l/cbCaioWwUHpBs7oO","pdf",408472,1,9,"English","en",105,"# Introduction\n## Hybrid model concept (TF-IDF + Word2Vec)\n## Research tasks and methodology\n## Data collection, preprocessing, and evaluation metrics","[{\"question\":\"Why is quality assessment for distance education difficult with traditional surveys?\",\"answer\":\"Traditional questionnaires and surveys often fail to provide a complete and fully objective picture. The paper argues that machine-learning and NLP-based analysis of text can improve accuracy and detail.\"},{\"question\":\"What feature extraction methods does the proposed hybrid model use?\",\"answer\":\"The model combines TF-IDF and Word2Vec. TF-IDF highlights essential terms, while Word2Vec represents words as dense vectors to reflect semantic meaning.\"},{\"question\":\"How does the paper evaluate the effectiveness of the hybrid model?\",\"answer\":\"It trains and evaluates a machine learning model using TF-IDF and Word2Vec features, then analyzes performance using various metrics. The study also includes experimental comparison of different feature extraction methods and their combinations.\"}]","Application of Machine Learning Methods to Analysis and Evaluation of Distance Education - Hybrid Model with TF-IDF and Word2Vec | PDF",1785734429,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-methods-to-analysis-and-evaluation-of-distance-education-hybrid-model-with-tf-idf-and-word2vec","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-methods-to-analysis-and-evaluation-of-distance-education-hybrid-model-with-tf-idf-and-word2vec/121224/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is quality assessment for distance education difficult with traditional surveys?","Question",{"text":75,"@type":76},"Traditional questionnaires and surveys often fail to provide a complete and fully objective picture. The paper argues that machine-learning and NLP-based analysis of text can improve accuracy and detail.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What feature extraction methods does the proposed hybrid model use?",{"text":80,"@type":76},"The model combines TF-IDF and Word2Vec. TF-IDF highlights essential terms, while Word2Vec represents words as dense vectors to reflect semantic meaning.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate the effectiveness of the hybrid model?",{"text":84,"@type":76},"It trains and evaluates a machine learning model using TF-IDF and Word2Vec features, then analyzes performance using various metrics. 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