vovrВысшее образование в России (Vysshee obrazovanie v Rossii = Higher Education in Russia)Vysshee Obrazovanie v Rossii = Higher Education in Russia0869-36172072-0459Moscow Polytechnic University10.31992/0869-3617-2023-32-10-133-150vovr-4619Research ArticleСтатьиОнлайн-образование после пандемии: исследование проблем и возможностей студентов инструментами больших данныхOnline Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Toolshttps://orcid.org/0000-0002-3553-2272БогдановаА. В.BogdanovaA. V.

Богданова Анна Владимировна – канд. пед. наук, начальник отдела технологий онлайн-образования,

445020, Самарская обл., г. Тольятти, Белорусская ул., 14.

Anna V. Bogdanova – Cand. Sci. (Pedagogical Sciences), Head of the Department of Online Education Technologies,

14 Belorusskaya str., Togliatti, 445020, Samara region.

a.bogdanova@tltsu.ruhttps://orcid.org/0000-0002-6069-779XАлександроваЮ. К.AleksandrovaYu. K.

Александрова Юлия Константиновна – мл. науч. сотрудник Центра прикладного анализа больших данных, 

634050, Томск, пр-т Ленина, 36.

Yuliya K. Aleksandrova – Junior Research Fellow, Center for Applied Big Data Analysis,

36 Lenina ave., Tomsk, 634050.

jalexandrova@data.tsu.ruhttps://orcid.org/0000-0002-5985-3724ГойкоВ. Л.GoikoV. L.

Гойко Вячеслав Леонидович – заведующий научно-исследовательской лабораторией прикладного анализа больших данных,

634050, Томск, пр-т Ленина, 36.

Vyacheslav L. Goiko – Head of the Research Laboratory for Applied Big Data Analysis,

36 Lenina ave., Tomsk, 634050.

goiko@data.tsu.ruhttps://orcid.org/0000-0001-6617-5346ОрловаВ. В.OrlovaV. V.

Орлова Вера Вениаминовна – д-р социол. наук, проф., заведующая кафедрой философии и социологии,

634034, Томск, пр-т Ленина, 40.

Vera V. Orlova – Dr. Sci. (Sociology), Professor of the Department of Philosophy and Sociology, 

40 Lenina ave., Tomsk, 634034.

orlova_vv@mail.ruТольяттинский государственный университетРоссияTogliatti State UniversityRussian FederationТомский государственный университетРоссияTomsk State UniversityRussian Federation2023291020233210133150Copyright © Богданова А.В., Александрова Ю.К., Гойко В.Л., Орлова В.В., 20232023Богданова А.В., Александрова Ю.К., Гойко В.Л., Орлова В.В.Bogdanova A.V., Aleksandrova Y.K., Goiko V.L., Orlova V.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vovr.elpub.ru/jour/article/view/4619

В статье представлен научно обоснованный подход к анализу больших объёмов данных цифровых следов студентов в социальных сетях, который позволяет эффективно выявлять возникающие и наиболее обсуждаемые проблемы у студентов, а также выделять болевые точки, дающие возможности роста, развития вузов и улучшения характеристик образовательного процесса, сопровождения студентов и т. д. Исследование основано на тематическом анализе сообщений, опубликованных в университетских сообществах в социальной сети «ВКонтакте» инструментами больших данных. Результаты исследования показали, что студенты российских вузов до сих пор сталкиваются с рядом проблем, включая слабую техническую инфраструктуру университетов, цифровое неравенство в доступе к онлайн-образованию и негативное отношение к дистанционному обучению.

Научная проблема исследования заключается в противоречии между существующим объёмом неструктурированных данных цифровых следов студентов в социальных сетях и отсутствием научно-обоснованного и апробированного методологического подхода к анализу и оценке этих объёмных данных, что создаёт препятствия для фундаментального исследования взаимосвязи между активностью студентов в социальных сетях и их удовлетворённостью качеством образовательного процесса. Практическая направленность определяется в проведении анализа данных с применением инструментов больших данных. Полученные результаты и научно обоснованные выводы полезны для разработки инновационных стратегий и инструментов оценки и поддержки студентов.

Результаты показывают, что отслеживание трендов на основе цифровых следов студентов в социальных сетях и инструментария больших данных даёт высокую точность аналитических данных и может стать основой для выявления проблемных ситуаций в отдельных вузах и отрасли в целом, для принятия решений и управления, основанного на данных.

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 .

удовлетворённость студентоввысшее образованиеинтеллектуальный анализ данныхбольшие данныеонлайн-образованиекачество образованияцифровой следсоциальные сетиstudent satisfactionhigher educationdata miningbig dataonline educationeducation qualitydigital footprintsocial networksИсследование выполнено при поддержке Программы развития ТГУ («Приоритет-2030»). Мы также выражаем искреннюю благодарность рецензентам журнала за внимание, уделенное нашей статьеThe study was carried out with the support of the TSU Development Program (“Priority-2030”). Authors also express their sincere gratitude to the journal’s reviewers for their attention to authors' article.ReferencesMarginson S. Imagining the global // Handbook of globalization and higher education: ed. by R. King, S. Marginson, R. Naidoo. 2011. P. 10–39. URL: https://www.researchgate.net/publication/285738663_Imagining_the_global (дата обращения: 20.04.2023).Marginson, S. (2011). Imagining the Global. In: R. King, S. Marginson, R. Naidoo (Eds.). Handbook of Globalization and Higher Education. Pp. 10-39. Available at: https://www.researchgate.net/publication/285738663_Imagining_the_global (accessed 04.20.2023).Chapleo C., O’Sullivan H. Contemporary thought in higher education marketing // Journal of Marketing for Higher Education. 2017. Vol. 27. No. 2. P. 159–161. DOI: 10.1080/08841241.2017.1406255Chapleo, C., O’Sullivan, H. Contemporary Thought in Higher Education Marketing. (2017). Journal of Marketing for Higher Education. Vol. 27, no. 2, pp. 159-161, doi: 10.1080/8841241.2017.1406255Горбунова Е.В. Выбытия студентов из вузов: исследования в России и США // Вопросы образования. 2018. №1. C. 110–131. DOI: 10.17323/1814-9545-2018-1-110-131Gorbunova, E.V. (2018). Students Leaving Universities: Studies in Russia and the USA. Voprosy obrazovaniya = Education Studies. No. 1, pp. 110-131, doi: 10.17323/1814-9545-2018-1-110-131Груздев И.А., Горбунова Е.В., Фрумин И.Д. Студенческий отсев в российских вузах: к постановке проблемы // Вопросы образования. 2013. №2 (октябрь). C. 67–81. DOI: 10.17323/1814-9545-2013-2-67-81Gruzdev, I.A., Gorbunova, E.V., Frumin, I.D. (2013). Student Dropout in Russian Universities: Towards the Formulation of the Problem. Voprosy obrazovaniya = Education Studies. No. 2 (October), pp. 67-81, doi: 10.17323/1814-9545-2013-2-67-81(In Russ., abstract in Eng.).Arria A.M., Garnier-Dykstra L.M., Caldeira K.M., Vincent K.B., Winick E.R., O’Grady K.E. Drug use patterns and continuous enrollment in college: results from a longitudinal study // Journal of Studies on Alcohol and Drugs. 2013. Vol. 74. No. 1. P. 71–83. DOI: 10.15288/jsad.2013.74.71Arria, A.M., Garnier-Dykstra, L.M., Caldeira, K.M., Vincent, K.B., Winick, E.R., O’Grady, K.E. (2013). Drug Use Patterns and Continuous Enrollment in College: Results from a Longitudinal Study. Journal of Studies on Alcohol and Drugs. Vol. 74, no. 1, pp. 71-83, doi: 10.15288/jsad.2013.74.71Дзюбан В.В. Проблема внедрения цифровых технологий в систему образования в XX–XXI в. // Архонт. 2021. №6 (27). C. 34–39. EDN: GHGFYH.Dzyuban, V.V. (2021). The Problem of Introducing Digital Technologies into the Education System in the XX-XXI Centuries. Arkhont [Archon]. No. 6 (27), pp. 34-39. Available at: https://elibrary.ru/download/elibrary_48021742_17885110.pdf (accessed 04.20.2023). (In Russ., abstract in Eng.).Vieira C., Parsons P., Byrd V. Visual learning analytics of educational data: A systematic literature review and research agenda // Computers & Education. 2018. No. 122. P. 119–135. DOI: 10.1016/j.compedu.2018.03.018Vieira, C., Parsons, P., Byrd, V. (2018). Visual Learning Analytics of Educational Data: A Systematic Literature Review and Research Agenda. Computers & Education. No. 122, p. 119-135, doi: 10.1016/j.compedu.2018.03.018Huda M., Maseleno A., Shahrill M., Jasmi K.A., Mustari I., Basiron B. Exploring adaptive teaching competencies in big data era // International Journal of Emerging Technologies in Learning (iJET). 2017. Vol. 12. No. 03. P. 68–83. DOI: 10.3991/ijet.v12i03.6434Huda, M., Maseleno, A., Shahrill, M., Jasmi, K. A., Mustari, I., Basiron, B. (2017). Exploring Adaptive Teaching Competencies in Big Data Era. International Journal of Emerging Technologies in Learning (iJET). Vol. 12, no. 03, pp. 68-83, doi: 10.3991/ijet.v12i03.6434Imani M., Montazer G.A. A survey of emotion recognition methods with emphasis on elearning environments // Journal of Network and Computer Applications. 2019. No. 147. Article no. 102423. DOI: 10.1016/j.jnca.2019.102423Imani, M., Montazer, G.A. (2019). A Survey of Emotion Recognition Methods with Emphasis on E-Learning Environments. Journal of Network and Computer Applications. No. 147, article no. 102423, doi: 10.1016/j.jnca.2019.102423Sultan P., Wong H.Y. Service quality in a higher education context: An integrated model // Asia Pacific Journal of Marketing and Logistics. 2012. Vol. 24. No. 5. P. 755–784. DOI: 10.1108/13555851211278196Sultan, P., Wong, H.Y. (2012). Service Quality in a Higher Education Context: An Integrated Model. Asia Pacific Journal of Marketing and Logistics. Vol. 24, no. 5, pp. 755-784, doi: 10.1108/13555851211278196Wulff A. Global Education Governance in the Context of COVID-19: Tensions and Threats to Education as a Public Good // Development. 2021. No. 64. P. 74–81. DOI: 10.1057/s41301-021-00293-1Wulff, A. (2021). Global Education Governance in the Context of COVID-19: Tensions and Threats to Education as a Public Good. Development. No. 64, pp. 74-81, doi: 10.1057/s41301-021-00293-1Raju D., Schumacker R. Exploring student characteristics of retention that lead to graduation in higher education using data mining models // Journal of College Student Retention: Research, Theory & Practice. 2015. No. 16. P. 563–591. DOI: 10.2190/CS.16.4.eRaju, D., Schumacker, R. (2015). Exploring Student Characteristics of Retention That Lead to Graduation in Higher Education Using Data Mining Models. Journal of College Student Retention: Research, Theory & Practice. No. 16, pp. 563-591, doi: 10.2190/CS.16.4.ePerez B., Castellanos C., Correal D. Applying data mining techniques to predict student dropout: a case study // 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI). 2018. P. 1–6. DOI: 10.1109/ColCACI.2018.8484847Perez, B., Castellanos, C., Correal, D. (2018). Applying Data Mining Techniques to Predict Student Dropout: A Case Study. In: 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI). Pp. 1-6, doi: 10.1109/ColCACI.2018.8484847Baek C., Doleck T. Educational data mining versus learning analytics: A review of publications from 2015 to 2019 // Interactive Learning Environments. 2021. P. 1–23. DOI: 10.1080/10494820.2021.1943689Baek, C., Doleck, T. (2021). Educational Data Mining Versus Learning Analytics: A Review of Publications from 2015 to 2019. Interactive Learning Environments. Pp. 1-23, doi: 10.1080/10494820.2021.1943689de Oliveira C.F., Sobral S.R., Ferreira M.J., Moreira F. How does learning analytics contribute to prevent students dropout in higher education: A systematic literature review // Big Data and Cognitive Computing. 2021. Vol. 5 (4). No. 64. P. 1–33. DOI: 10.3390/bdcc5040064de Oliveira, C.F., Sobral S.R., Ferreira M.J., Moreira F. (2021). How Does Learning Analytics Contribute to Prevent Students Dropout in Higher Education: A Systematic Literature Review. Big Data and Cognitive Computing. Vol. 5 (4), no. 64, pp. 1-33, doi: 10.3390/bdcc5040064Larrabee Sønderlund A., Hughes E., Smith J. The efficacy of learning analytics interventions in higher education: A systematic review//British Journal of Educational Technology. 2019. Vol. 50. No. 5. P. 2594–2618. DOI: 10.1111/bjet.12720ReturnLarrabee Sønderlund, A., Hughes, E., Smith, J. (2019). The Effectiveness of Learning Analytics Interventions in Higher Education: A Systematic Review. British Journal of Educational Technology. Vol. 50, no. 5, pp. 2594-2618, doi: 10.1111/bjet.12720ReturnRastrollo-Guerrero J.L., Gómez-Pulido J.A., Durán-Domínguez A. Analyzing and Predicting students performance by means of machine learning: A review // Applied Sciences. 2020. Vol. 10. No. 3. Article no. 1042. P. 1–16. DOI: 10.3390/app10031042Rastrollo-Guerrero, J.L., Gómez-Pulido, J.A., Durán-Domínguez, A. (2020). Analyzing and Predicting Student Performance by Means of Machine Learning: A Review. Applied Sciences. Vol. 10, no. 3, article no. 1042, pp. 1-16, doi: 10.3390/app10031042Goel Y., Goyal R. On the effectiveness of selftraining in MOOC drop-out prediction // Open Computer Science. 2020. Vol. 10. No. 1. P. 246–258. DOI: 10.1515/comp-2020-0153Goel, Y., Goyal, R. (2020). On The Effectiveness of Self-Training in MOOC Drop-Out Prediction. Open Computer Science. Vol. 10, no. 1, pp. 246-258, doi: 10.1515/comp-2020-0153Guo S., Zeng D., Dong S. Pedagogical data analysis via federated learning toward education 4.0 // American Journal of Education and Information Technology. 2020. Vol. 4. No. 2. P. 55–56. DOI: 10.11648/j.ajeit.20200402.13Guo, S., Zeng, D., Dong, S. (2020). Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. American Journal of Education and Information Technology. Vol. 4, no. 2, pp. 55-56, doi: 10.11648/j.ajeit.20200402.13Hasan R., Palaniappan S., Mahmood S., Abbas A., Sarker K.U., Sattar M.U. Predicting student performance in higher educational institutions using video learning analytics and data mining techniques // Applied Sciences (Switzerland). 2020. Vol. 10. No. 11. Article no. 3894. DOI: 10.3390/app10113894Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K.U., Sattar, M.U. (2020). Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques. Applied Sciences (Switzerland). Vol. 10, no. 11, article no. 3894, doi: 10.3390/app10113894Mai T.T., Bezbradica M., Crane M. Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data // Future Generation Computer Systems. 2022. No. 127. P. 42–55. DOI: 10.1016/j.future.2021.08.026Mai, T.T., Bezbradica, M., Crane, M. (2022). Learning Behaviors Data in Programming Education: Community Analysis and Outcome Prediction with Cleaned Data. Future Generation Computer Systems. No. 127, pp. 42-55, doi: 10.1016/j.future.2021.08.026Von Hippel P.T., Hofflinger A. The data revolution comes to higher education: identifying students at risk of dropout in Chile //Journal of Higher Education Policy and Management. 2020. 24 p. DOI: 10.1080/1360080X.2020.1739800Von Hippel, P.T., Hofflinger, A. (2020). The Data Revolution Comes to Higher Education: Identifying Students at Risk Of Dropout in Chile. Journal of Higher Education Policy and Management. 24 p., doi: 10.1080/1360080X.2020.1739800Cannistrà M., Masci C., Ieva F., Agasisti, T., Paganoni A.M. Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach//MOX-Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Via Bonardi (Milan). 2020. No. 41. 33 p. URL: https://www.mate.polimi.it/biblioteca/add/qmox/41-2020.pdf (дата обращения: 25.04.2023).Cannistrà, M., Masci, C., Ieva, F., Agasisti, T., Paganoni, A.M. (2020). Not the magic algorithm: modeling and early-predicting students dropout through machine learning and multilevel approach. In: MOX-Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Via Bonardi (Milan). No. 41. 33 p. Available at: https://www.mate.polimi.it/biblioteca/add/qmox/41-2020.pdf (accessed 04.20.2023).Azcona D., Hsiao I.H., Smeaton A.F. Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints // User Modeling and UserAdapted Interaction. 2019. No. 29. P. 759–788. DOI: 10.1007/s11257-019-09234-7Azcona, D., Hsiao, I.H., Smeaton, A.F. (2019). Detecting Students-At-Risk in Computer Programming Classes with Learning Analytics from Students’ Digital Footprints. User Modeling and User-Adapted Interaction. No. 29, pp. 759-788, doi: 10.1007/s11257-019-09234-7Miller Z., Dickinson B., Hu W. Gender prediction on Twitter using stream algorithms with ngram character features// International Journal of Intelligence Science. 2012. No. 2. P. 143–148. DOI: 10.4236/ijis.2012.224019Miller, Z., Dickinson, B., Hu, W. (2012). Gender Prediction on Twitter Using Stream Algorithms With N-Gram Character Features. International Journal of Intelligence Science. No. 2, pp. 143-148, doi: 10.4236/ijis.2012.224019Поливанова К.Н., Смирнов И.Б. Что в профиле тебе моем: Данные «ВКонтакте» как инструмент изучения интересов современных подростков // Вопросы образования. 2017. No. 2. C. 134–152. DOI: 10.17323/1814-9545-2017-2-134-152Polivanova, K.N., Smirnov, I.B. (2017). What’s in My Profile for You: Vkontakte Data as a Tool for Studying the Interests of Modern Teenagers. Voprosy obrazovaniya = Education Studies. No. 2, pp. 134-152, doi: 10.17323/1814-9545-2017-2-134-152Gosling S.D., Augustine A., Vazire S., Holtzman N., Gaddis S. Manifestations of personality in online social networks//Cyberpsychology, Behavior, and Social Networking. 2011. Vol. 14. No. 9. P. 483–488. DOI: 10.1089/cyber.2010.0087Gosling, S.D., Augustine, A., Vazire, S., Holtzman, N., Gaddis, S. (2011). Manifestations of Personality in Online Social Networks. Cyberpsychology, Behavior, and Social Networking. Vol. 14, no. 9, pp. 483-488, doi: 10.1089/cyber.2010.0087Степаненко А.А., Резанова З.И., Гойко В.Л. Автоматическая классификация контента персональных страниц пользователей социальной сети «Вконтакте» как маркеров профессиональных интересов абитуриента // Гуманитарная информатика. 2018. No. 15. C. 20–26. DOI: 10.17223/23046082/15/1Stepanenko, A.A., Rezanova, Z.I., Goiko, V.L. (2018). Automatic Classification of the Content of Personal Pages of Users of the Social Network “Vkontakte” as Markers of the Professional Interests of an Applicant. Humanitarian Informatics. No. 15, pp. 20-26, doi:10.17223/23046082/15/1Dascalu M., Popescu E., Becheru A., Crossley S., Trausan-Matu S. Predicting academic performance based on students’ blog and microblog posts // European Conference on Technology Enhanced Learning. 2016. P. 370–376. DOI: 10.1007/978-3-319-45153-4_29Dascalu, M., Popescu, E., Becheru, A., Crossley, S., Trausan-Matu, S. (2016). Predicting Academic Performance Based on Students’ Blog and Microblog Posts. European Conference on Technology Enhanced Learning. Pp. 370-376, doi: 10.1007/978-3-319-45153-4_29Hernández-de-Menéndez M., Morales-Menendez R., Escobar C.A., Ramírez Mendoza R.A. Learning analytics: state of the art // International Journal on Interactive Design and Manufacturing. 2022. No. 16. P. 1209–1230. DOI: 10.1007/s12008-022-00930-0Hernández-de-Menendez, M., Morales-Menendez, R., Escobar, C.A., Ramírez Mendoza, R.A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing. No. 16, pp. 1209-1230, doi: 10.1007/s12008-022-00930-0Brooks C.A., Thompson C.D.S. Chapter 5 : Predictive Modelling in Teaching and Learning. 2017. URL: https://www.semanticscholar.org/paper/Chapter-5-%3A-Predictive-Modellingin-Teaching-and-BrooksThompson/2cd4901b07f3562f98e1e56dc5712e8bc03bdc2e (дата обращения: 20.04.2023).Brooks, C.A., Thompson, C.D.S. (2017). Chapter 5: Predictive Modeling in Teaching and Learning. Available at: https://www.semanticscholar.org/paper/Chapter-5-%3A-Predictive-Modelling-in-Teaching-and-BrooksThompson/2cd4901b07f3562f98e1e56dc5712e8bc03bdc2e (accessed 04.20.2023).

The authors declare that there are no conflicts of interest present.