Umudike, Nigeria
Ekaterinburg, Ekaterinburg, Russian Federation
Saint Petersburg, Russian Federation
Digital educational technologies (DET) have confidently taken the position of a structural element of the modern higher education system. Despite the success of digitalization of universities, there remain problems associated with high costs and technical aspects of the introduction of user-friendly and high-quality digital platforms. This actualizes the problem of optimizing university budgets for digitalization. The purpose of the article is to study the tools of complex semantic analysis and sentiment analysis in Russian universities as a tool for evaluating the effectiveness of DET. The objectives of the study were: a review of the literature on the stated problem; development of a research methodology; conducting a comprehensive semantic analysis and sentiment analysis in Russian universities; evaluating the effectiveness of DET; developing recommendations to improve the effectiveness of DET. Research methods: a hybrid quantitative and qualitative approach was used; including systematic reviews, the use of vocabulary and natural language processing (NLP) for sentiment analysis and semantics. The results of the study show that although 88% of universities have learning management systems (LMS), only 45% effectively use them for educational purposes. Only 44% of universities have licenses for collaboration software (Zoom), and 13% do not have the necessary digital infrastructure (high-speed Internet). These gaps indicate significant obstacles to the effective use of DET. The results of sentiment analysis show that students generally have a positive attitude towards digital learning platforms, and sentiment analysis methods based on deep learning demonstrate high effectiveness of DET. The authors recommend improving the digital infrastructure, raising the level of training of teaching staff and students, as well as developing targeted strategies for better integration of DET.
Digital Educational Technologies (DET); Learning Management Systems (LMS); Digital Literacy; Sentiment Analysis; Semantic Analysis; Digital Infrastructure; Higher Education in Russia; Constructivist Learning Theory (CLT); Effectiveness of digital educational technologies; Effectiveness DET
1. Abuhassna, H., Al-rahmi, W., Yahya, N., Zakaria, M., Kosnin, A., & Darwish, M. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction // International Journal of Educational Technology in Higher Education. – 2020. https://doi.org/10.1186/ s41239-020-00216-z
2. Alencar, G., & Netto, J. F. M. A comprehensive review on sentiment analysis in social networks // Expert Systems with Applications. – 2020. – 142, 113018. https://doi.org/10.1016/j.eswa.2019.113018
3. Alenezi, M. Digital Learning and Digital Institution in Higher Education. Education Sciences. – 2023. 13(1). 88. https://doi.org/10.3390/educsci13010088
4. Alenezi, M., Wardat, S., & Akour, M. The Need of Integrating Digital Education in Higher Education: Challenges and Opportunities // Sustainability. – 2023. – 5(6), 4782. https://doi.org/10.3390/su15064782
5. Al-Hail, M., Zguir, M. F., & Koç, M. University students’ and educators’ perceptions on the use of digital and social media platforms: A sentiment analysis and a multi-country review // iScience. – 2023. – 26(8), 107322. https://doi.org/10.1016/j.isci.2023.107322
6. Almazova, N., Krylova, E., Rubtsova, A., & Odinokaya, M. Challenges and Opportunities for Russian Higher Education amid COVID-19: Teachers’ Perspective // Education Sciences. – 2020. https://doi. org/10.3390/educsci10120368
7. Alshammary, F. M., & Alhalafawy, W. S. Digital Platforms and the Improvement of Learning Outcomes: Evidence Extracted from Meta-Analysis. Sustainability. – 2023. DOI:https://doi.org/10.3390/su15021305
8. Clark, A., Nong, H., Zhu, H., & Zhu, R. Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic // China Economic Review. – 2020. DOI:https://doi.org/10.1016/j. chieco.2021.101629
9. Consoli, S., Barbaglia, L., & Manzan, S. Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon // Data Collection & Empirical Methods. – 2021. https://doi.org/10.2139/ssrn.3766194
10. Drozdikova-Zaripova, A. R., Valeeva, R. A., & Latypov, N. R. The impact of isolation measures during COVID-19 pandemic on Russian students’ motivation for learning. // Education Sciences. – 2021. – 11(11), 722. http://dx.doi.org/10.3390/educsci11110722
11. Drugova, E., Zhuravleva, I., Aiusheeva, M., & Grits, D. Toward a model of learning innovation integration: TPACK-SAMR based analysis of the introduction of a digital learning environment in three Russian universities // Education and Information Technologies. – 2021. – 26, 10514. https://doi.org/10.1007/ s10639-021-10514-2
12. Grimalt-Álvaro, C., & Usart, M. Sentiment analysis for formative assessment in higher education: a systematic literature review // Journal of computing in higher education. – 2023. 1-36. https://doi.org/10.1007/ s12528-023-09370-5.
13. Kastrati, Z., Dalipi, F., Imran, A. S., Nuçi, K. P., & Wani, M. A. Sentiment analysis of students’ feedback with NLP and deep learning: A systematic mapping study // Applied Sciences. – 2021. https://doi.org/10.3390/APP11093986
14. Ke, Z., Sheng, J., Li, Z., Silamu, W., & Guo, Q. Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations // IEEE Access. – 2021. https://doi.org/10.1109/ACCESS.2020.3048088 15. Kitchenham, B. Procedures for performing systematic reviews. Keele. – UK: Keele University, 2004. – 33(2004), 1-26.
15. Kliziene, I. et al. The Impact of the Virtual Learning Platform EDUKA on the Academic Performance of Primary School Children // Sustainability. – 2021. DOI:https://doi.org/10.3390/SU13042268
16. Li, X., Lin, X., Zhang, F., & Tian, Y. What Matters in Online Education: Exploring the Impacts of Instructional Interactions on Learning Outcomes. Frontiers in Psychology. // 2022. DOI: 10.3389/ fpsyg.2021.792464
17. Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. The PRISMA statement for reporting systematic reviews and metaanalyses of studies that evaluate healthcare interventions: Explanation and elaboration // BMJ. – 2009. 339, b2700–b2700.
18. Liu, Z. J., Tretyakova, N., Fedorov, V., & Kharakhordina, M. Digital Literacy and Digital Didactics as the Basis for New Learning Models Development // International Journal of Emerging Technologies in Learning. – 2020. doihttps://doi.org/10.3991/ijet.v15i14.14669
19. Liu, Z.-Y., Lomovtseva, N., & Korobeynikova, E. Online Learning Platforms: Reconstructing Modern Higher Education // International Journal of Emerging Technologies in Learning. – 2020. DOI:https://doi.org/10.3991/ijet. v15i13.14645
20. Mandaar B. Pande, S. Bharathi. Theoretical foundations of design thinking – A constructivism learning approach to design thinking // Thinking Skills and Creativity. – 2020. DOI:https://doi.org/10.1016/j.tsc.2020.100637
21. Mohd, M., Javeed, S., Nowsheena, & Wani, M. A. Sentiment analysis using lexico-semantic features // Journal of Information Science. – 2022. https://doi.org/10.1177/01655515221124016
22. Mostafa, M. M. An investigation of sentiment analysis in educational data: A lexicon-based approach // Educational Technology & Society. – 2020). 23(4), 52-65. https://www.jstor.org/stable/26828279
23. Nikolić, N., Grljević, O., & Kovačević, A. Aspect-based sentiment analysis of reviews in the domain of higher education // The Electronic Library. – 2020. https://doi.org/10.1108/el-06-2019-0140
24. Noor, U., Younas, M., Aldayel, H. S., Menhas, R., & Xu, Q. Learning behavior, digital platforms for learning and its impact on university students’ motivations and knowledge development // Frontiers in Psychology. 2022. https://doi.org/10.3389/fpsyg.2022.933974
25. Peskova, O., Sharkevich, I., Belskikh, I., & Boriskina, T. B. Assessment of the digital activity of universities: Russian educational platform of the Yuryat // Perspectives of Science and Education. – 2022. 2, 37. https://doi.org/10.32744/pse.2022.2.37
26. Phillips, M., Reed, J. B., Zwicky, D., & Van Epps, A. S. A scoping review of engineering education systematic reviews // Journal of Engineering Education. – 2023.
27. Pinto, M., & Leite, C. Digital technologies in support of students learning in Higher Education: literature review // Digital Education Review. – 2020. 37, 343-360. https://doi.org/10.1344/der.2020.37.343360
28. Potapova, R. K., & Potapov, V. V. Sentiment analysis of digital communication // Vestnik Moskovskogo gosudarstvennogo lingvisticheskogo universiteta. Gumanitarnye nauki. – 2023. (4 (872)), 86-96. DOIhttps://doi.org/10.52070/2542-2197_2023_4_872_86
29. Pradana, M., Rintaningrum, R., Kosov, M., Bloshenko, T., Rogova, T., & Singer, N. Increasing the effectiveness of educational technologies in the foreign languages learning process by linguistic students (comparative analysis of Russian, Indonesian and Egyptian experience) // In Frontiers in Educatio. – 2022. Vol. 7, p. 1011842. https://doi.org/10.3389/feduc.2022.1011842
30. Ronzhina, N., Kondyurina, I., Voronina, A., Igishev, K., & Loginova, N. Digitalization of Modern Education: Problems and Solutions // International Journal of Emerging Technologies in Learning. – 2021. 16(4), 18203. https://doi.org/10.3991/IJET.V16I04.18203
31. Sanda, M. Impact of digitised ‘teaching-learning’ virtual platforms on tertiary students’ learning objectives and teaching outcomes // Theoretical Issues in Ergonomics Science. – 2022. DOI:https://doi.org/10.1080/1463922x.2022.2161114
32. Sanglerdsinlapachai, N., Plangprasopchok, A., Ho, T., & Nantajeewarawat, E. Improving sentiment analysis on clinical narratives by exploiting UMLS semantic types // Artificial Intelligence in Medicine. – 2021. https://doi.org/10.1016/j.artmed.2021.102033
33. Schurig, T., Zambach, S., Mukkamala, R. R., & Petry, M. Aspect-based Sentiment Analysis for University Teaching Analytics. 2022.https://aisel.aisnet.org/ecis2022_rp/135utm_source=aisel.aisnet.org%2Fecis2022_rp%2F135&utm_medium=PDF&utm_campaign=PDFCoverPages
34. Smetanin, S. The applications of sentiment analysis for Russian language texts: Current challenges and future perspectives. IEEE Access. – 2020. https://doi.org/10.1109/ACCESS.2020.3002215
35. Sun, P. P., & Chen, L. Y. Digital technologies in higher education: Implementation and assessment. Journal of Educational Technology & Society. – 2016. 19(1), 203-214. https://www.jstor.org/stable/ jeductechsoci.19.1.203
36. Vertakova, Y., Gorodilov, M., Popov, V., Bulgakova, I., & Alexandrova, T. Development of remote learning system in higher educational institutions of the Russian Federation: from local practice to integration into the digital university model // International Journal of Technology Enhanced Learning. – 2022. https:// doi.org/10.1504/ijtel.2022.10048082
37. Wan, H., & Tang, S. Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining // Journal of Intelligent and Fuzzy Systems. – 2021. https://doi. org/10.3233/JIFS-219047
38. Xiang, Q., Huang, T., Zhang, Q., Li, Y., Tolba, A. M., & Bulugu, I. E. A novel sentiment analysis method based on multi-scale deep learning // Mathematical Biosciences and Engineering. – 2023. https://doi. org/10.3934/mbe.2023385
39. Zhai, G., Yang, Y., Wang, H., & Du, S. Multi-attention fusion modeling for sentiment analysis of educational big data // Big Data Mining and Analytics. – 2020. https://doi.org/10.26599/bdma.2020.9020024
40. Zhang, Z., Zhang, J., & Guo, C. Sentiment analysis: A lexicon-based approach // International Journal of Computer Applications. – 2012. 38(3), 22-28. https://doi.org/10.5120/4632-6797
