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

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 2 (2025)

DOI
10.11591/ijece.v15i2.pp2172-2180
ISSN
2722-2578, 2088-8708
Published
2025-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

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.

Collaboration network of the authors

26 authors · 97 co-authorship links · 47 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • L. N. Gumilyov Eurasian National University
  • Astana Medical University
  • National Academy of Education named after Y. Altynsarin
  • Caspian University
  • Abylkas Saginov Karaganda Technical University
  • M. Kh Dulati Taraz State University
  • Khalel Dosmukhamedov Atyrau University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Makpal ZhartybayevaL. N. Gumilyov Eurasian National University2
Sadvakassov Arman AshatulyL. N. Gumilyov Eurasian National University2
Zhanar LamashevaL. N. Gumilyov Eurasian National University2
Kerimkhulle Seyit YesilbayulyAffiliation unknown2
Oralkhanov Berdibek OralkhanulyL. N. Gumilyov Eurasian National University2
Ayagoz MukhanovaL. N. Gumilyov Eurasian National University1
Valentina E. MakhatovaKhalel Dosmukhamedov Atyrau University1
Madiyar BaitemirovL. N. Gumilyov Eurasian National University1
Tleugaisha OspanovaL. N. Gumilyov Eurasian National University1
Azamat AmirovAbylkas Saginov Karaganda Technical University1
Bolat TassuovM. Kh Dulati Taraz State University1
Assemgul KaipovaAstana Medical University1
Ainur OrazayevaL. N. Gumilyov Eurasian National University1
Jamalbek TussupovL. N. Gumilyov Eurasian National University1
Gulmira ShangytbayevaAktobe Regional State University named after K.Zhubanov1
Assem GalymovaAffiliation unknown1
Ulzhalgas ZhunissovaAstana Medical University1
Aliya S. TergeussizovaAlmaty University of Power Engineering and Telecommunications1
Arailym TleubayevaAstana IT University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Application of machine learning methods to analysis and evaluation of distance education</dc:title><dc:creator>Mukhiyadin, Ainur</dc:creator><dc:creator>Mukasheva, Manargul</dc:creator><dc:creator>Makhazhanova, Ulzhan</dc:creator><dc:creator>Kassekeyeva, Aislu</dc:creator><dc:creator>Azieva, Gulmira</dc:creator><dc:creator>Kenzhebayeva, Zhanat</dc:creator><dc:creator>Abdrakhmanova, Alfiya</dc:creator><dc:description>&lt;jats:p&gt;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.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v15i2.pp2172-2180</dc:identifier><dc:identifier>10.11591/ijece.v15i2.pp2172-2180</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 2 (2025); 2172</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/37476/18135</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>