Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools
This paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for gro...
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| Format: | Article |
| Language: | English |
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Moscow Polytechnic University
2023-10-01
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| Series: | Высшее образование в России |
| Subjects: | |
| Online Access: | https://vovr.elpub.ru/jour/article/view/4619 |
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| author | A. V. Bogdanova Yu. K. Aleksandrova V. L. Goiko V. V. Orlova |
| author_facet | A. V. Bogdanova Yu. K. Aleksandrova V. L. Goiko V. V. Orlova |
| author_sort | A. V. Bogdanova |
| collection | DOAJ |
| description | This paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for growth, development of universities and improvement of the characteristics of the educational process, support for students etc. The study is based on a thematic analysis of messages published in university communities on the VKontakte social network using big data tools. The study results showed that Russian university students still face a number of challenges, including weak technical infrastructure at universities, a digital divide in access to online education, and negative attitudes towards distance learning.The scientific problem of the study is the contradiction between the existing volume of unstructured data of students’ digital traces in social networks and the lack of a scientifically-based and proven methodological approach to the analysis and evaluation of this voluminous data, which creates obstacles to fundamental research into the relationship between students’ activity in social networks and their satisfaction quality of the educational process. The practical focus is determined in conducting data analysis using big data tools. The findings and evidence-based implications are useful for developing innovative strategies and tools for assessing and supporting students.The results show that the use of big data tools for tracking trends based on digital traces of students on social networks provides highly accurate analytical data and can become the basis for identifying problematic situations in individual universities and the industry as a whole, for data-driven decision-making and management . |
| format | Article |
| id | doaj-art-14b24c53939643a9977bc2b51288c75d |
| institution | Kabale University |
| issn | 0869-3617 2072-0459 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Moscow Polytechnic University |
| record_format | Article |
| series | Высшее образование в России |
| spelling | doaj-art-14b24c53939643a9977bc2b51288c75d2025-02-01T13:14:32ZengMoscow Polytechnic UniversityВысшее образование в России0869-36172072-04592023-10-01321013315010.31992/0869-3617-2023-32-10-133-1502420Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data ToolsA. V. Bogdanova0Yu. K. Aleksandrova1V. L. Goiko2V. V. Orlova3Togliatti State UniversityTomsk State UniversityTomsk State UniversityTomsk State UniversityThis paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for growth, development of universities and improvement of the characteristics of the educational process, support for students etc. The study is based on a thematic analysis of messages published in university communities on the VKontakte social network using big data tools. The study results showed that Russian university students still face a number of challenges, including weak technical infrastructure at universities, a digital divide in access to online education, and negative attitudes towards distance learning.The scientific problem of the study is the contradiction between the existing volume of unstructured data of students’ digital traces in social networks and the lack of a scientifically-based and proven methodological approach to the analysis and evaluation of this voluminous data, which creates obstacles to fundamental research into the relationship between students’ activity in social networks and their satisfaction quality of the educational process. The practical focus is determined in conducting data analysis using big data tools. The findings and evidence-based implications are useful for developing innovative strategies and tools for assessing and supporting students.The results show that the use of big data tools for tracking trends based on digital traces of students on social networks provides highly accurate analytical data and can become the basis for identifying problematic situations in individual universities and the industry as a whole, for data-driven decision-making and management .https://vovr.elpub.ru/jour/article/view/4619student satisfactionhigher educationdata miningbig dataonline educationeducation qualitydigital footprintsocial networks |
| spellingShingle | A. V. Bogdanova Yu. K. Aleksandrova V. L. Goiko V. V. Orlova Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools Высшее образование в России student satisfaction higher education data mining big data online education education quality digital footprint social networks |
| title | Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools |
| title_full | Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools |
| title_fullStr | Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools |
| title_full_unstemmed | Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools |
| title_short | Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools |
| title_sort | online education after the pandemic student problems and opportunities research using big data tools |
| topic | student satisfaction higher education data mining big data online education education quality digital footprint social networks |
| url | https://vovr.elpub.ru/jour/article/view/4619 |
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