Mathematics e-learning in the conditions of bilinguality
https://doi.org/10.32517/0234-0453-2022-37-4-25-33
Abstract
The world is going through a period of the rapid development of electronic learning systems. In addition, the creation of a digital educational environment and the formation of scientific ideas about using such systems in the educational process are currently taking place. This is of particular importance when teaching small-numbered indigenous peoples living in bilingual and multilingual regions. The article aims to develop an approach to organizing mathematics e-learning for students in a bilingual environment using mechanisms for adapting educational content to specific linguistic features. The study used scientific literature analysis and empirical methods. We suggest that adaptive e-learning courses be a means of e-learning in mathematics. They enable students to pursue an individual educational route and ensure content adaptation according to students’ language and the national peculiarities of their perception of learning materials. The result of the present study is an individual educational route created in an adaptive e-learning course in mathematics in a bilingual environment. In the future, the results of the research can become an element in the methodological system of personalized adaptive learning aimed at the preparation of a university student in a bilingual environment.
About the Authors
Yu. V. VainshteinRussian Federation
Yulia V. Vainshtein, Doctor of Sciences (Education), Docent, Professor at the Department of Applied Mathematics and Computer Safety, Institute of Space and Information Technologies
Krasnoyarsk
M. V. Noskov
Russian Federation
Mihail V. Noskov, Doctor of Sciences (Phys.-Math.), Professor, Professor at the Department of Applied Mathematics and Computer Safety, Institute of Space and Information Technologies
Krasnoyarsk
V. A. Shershneva
Russian Federation
Victoria A. Shershneva, Doctor of Sciences (Education), Professor, Professor at the Department of Applied Mathematics and Computer Safety, Institute of Space and Information Technologies
Krasnoyarsk
M. V. Tanzy
Russian Federation
Mengy V. Tanzy, Candidate of Sciences (Education), Docent, Head of the Department of Algebra and Geometry
Kyzyl, The Republic of Tuva
References
1. Uvarov A. Yu., Dvoreckaya I. V., Frumin I. D., Gejbl E., Karlov I. A., Merzalova T. A., Sergomanov P. A., Zaslavsky I. M. Difficulties and prospects of digital transformation of education. Moscow, NIU VShE; 2019. (In Russian.) DOI: 10.17323/978-5-7598-1990-5
2. Elliott S. W. Computers and the future of skill demand. Paris, OECD Publishing, France; 2017.
3. Andryushkova O., Grigoriev S. The influence online learning quality criteria selection on negentropy. IEELMDTE 2020 — Proceedings of the 4th International Conference on Informatization of Education and E-Learning Methodology: Digital Technologies in Education. Krasnoyarsk; 2020;(2770):127–139.
4. Peng H., Ma S., Spector J. M. Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments. 2019;6(9). DOI: 10.1186/s40561-019-0089-y
5. Fisher J., White J. Takeaways from the 2017 blended and personalized learning conference. Available at: https://www.gettingsmart.com/2017/10/17/takeaways-from-the2017-blended-and-personalized-learning-conference/
6. Komleva N.V., Vilyavin D.A. Digital platform for creating personalized adaptive online courses. Open Education. 2020;24(2):65–72. DOI: 10.21686/1818-4243-2020-2-65-72
7. Skinner B.F. Teaching machines. Science . 1958;(128(3330)):969–977. DOI: 10.1126/science.128.3330.969
8. Brusilovsky P. Adaptive and intelligent technologies for web-based education. Künstliche Intelligenz. 1999;13(4):19–25. Available at: http://www2.sis.pitt.edu/~peterb/papers/KI-review.pdf
9. Rizvi S. et al. Culturally adaptive learning design: A mixed-methods study of cross-cultural learning design preferences in MOOCs. Open World Learning. Routledge. 2022:103–115. DOI:10.4324/9781003177098-9
10. Dolasinski M. J., Reynolds J. Microlearning: A new learning model. Journal of Hospitality & Tourism Research. 2020;44(3):551–561. DOI: 10.1177/1096348020901579
11. Šimko M., Bielikova M. Lightweight domain modeling for adaptive web-based educational system. Journal of Intelligent Information Systems. 2019;(52):165–190. DOI: 10.1007/s10844-018-0518-3
12. Bray B., McClaskey K. Make learning personal: The what, who, WOW, where, and why. Thousand Oaks, California, Corwin Press; 2014.
13. Tayebinik M., Puteh M. Blended learning or e-learning? International Magazine on Advances in Computer Science and Telecommunications. 2012;3(1):103–110. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2282881
14. Vainshtein Yu. V., Shershneva V. A., Esin R. V., Noskov M. V. Individualisation of education in terms of e-learning: Experience and prospects. Journal of Siberian Federal University. Humanities and Social Sciences. 2019;12(9):1753–1770. DOI: 10.17516/1997-1370-0481
15. Nazarova V. Y., Ostapenko M. S. Individual educational paths. E3S Web of Conferences. 2021;(266(2)):05001. DOI: 10.1051/e3sconf/202126605001
16. El-Sabagh H. A. Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education. 2021;18(1):1–24. DOI: 10.1186/s41239-021-00289-4
17. Ilyin V. D. The model of the cooperative problem solver based on digital twins. Sistems and Means of Informatics. 2019;29(2):172–179. DOI: 10.14357/08696527190215
18. Zaharova I. G. Machine learning methods of providing informational management support for students’ professional development. Education and Science Journal. 2018;20(9):91– 114. DOI: 10.17853/1994-5639-2018-9-91-114
19. Tsibulskii G. M., Vaynshteyn Yu. V., Esin R. V. Developing adaptive e-learning courses in the LMS Moodle. Krasnoyarsk; 2018.
20. Dagger D., Wade V., Conlan O. Personalisation for all: Making adaptive course composition easy. Educational Technology and Society. 2005;8(3):9–25. Available at: https:// www.jstor.org/stable/jeductechsoci.8.3.9
21. Taryma A. K., Shershneva V. A., Vainshtein Y. V. Development of professional ICT competence for future teachers of the Tuva republic under the conditions of bilingualism. Perspectives of Science and Education. 2019;40(4):27–90. DOI: 10.32744/pse.2019.4.7.
22. Vainshtein J., Noskov M., Shershneva V., Tanzy M. Adaptation of educational content when learning mathematics in bilingual condition. IEELM-DTE 2020 — Proceedings of the 4th International Conference on Informatization of Education and E-Learning Methodology: Digital Technologies in Education. Krasnoyarsk; 2020;(2770):65–71. Available at: http://ceur-ws.org/Vol-2770/paper9.pdf
23. Shershneva V., Vainshtein Y., Kochetkova T. Adaptive system of web-based teaching. Program Systems: Theory and Applications. 2018;9(4):179–197. DOI:10.25209/2079-33162018-9-4-159-177
24. Lamajaa Ch. K. Tuvinology: the new horizons. M., Librocom Publishers; 2013.
25. Vainshtein J., Esin R., Tsibulsky G. Constructing domain model based on logical and epistemological analysis. IEELM-DTE 2020 — Proceedings of the 4th International Conference on Informatization of Education and E-Learning Methodology: Digital Technologies in Education. Krasnoyarsk; 2020;(2770):140–146. Available at: http://ceur-ws.org/Vol-2770/paper18.pdf
26. Tanzy M. V., Saaya S. K., Shershneva V. A., Vainshtein Y. V., Ondar C. M. The yurt as a geometric model in teaching mathematics. The New Research of Tuva. 2020;(4):80–91. DOI: 10.25178/nit.2020.4.6
Review
For citations:
Vainshtein Yu.V., Noskov M.V., Shershneva V.A., Tanzy M.V. Mathematics e-learning in the conditions of bilinguality. Informatics and education. 2022;37(4):25-33. https://doi.org/10.32517/0234-0453-2022-37-4-25-33