DIGITAL FOOTPRINT IN EDUCATION: FROM SCIENCE TO SOCIETY
DOI: 10.23951/2307-6127-2022-5-9-19
The application of predictive systems in education based on the use of big data technologies through the management of the digital footprint of students is discussed. The main attention is paid to the accepted managerial decisions. Issues of a technical plan, methodological nature, and legal regulation are not considered in the paper. The current trends in the formation of a digital footprint of students are described, the risks and challenges of introducing digital technologies into the educational sphere are formulated. Two approaches to optimizing the collected data are described: the gamification of education with the creation of a data collection environment and the use of specialized approaches in data processing. With regard to the second approach, the important role of a priori algorithms and expert assessments used in the process of processing the digital footprint has been revealed. A parallel is drawn with the use of big data in science, the importance of repeatedly accessing data and the use of proven methods for extracting information from unstructured data lakes is shown. It is shown that in the educational sphere, digitalization processes are expressed in the strengthening of the role of external stakeholders not related to the state. These trends come into conflict with state interests which lead to the active intervention of the authorities in the educational process. According to the authors, there is a prospect of forced formation of a digital footprint. In order to solve the emerging difficulties associated with the conflict between social and technical, it is proposed to focus on the development of a digital culture and the widespread introduction of the ethics of handling big data.
Keywords: digital footprint, big data, education, digitalization, models
References:
1. Sedova A. P., Kryukova A. A. Primeneniye teknologii Big Data v obrazovanii [Application of Big Data technology in education]. Science Time, 2015, no. 11 (23). pp. 505–509 (in Russian).
2. Fischer C., Pardos Z., Baker R. S., Williams J. J., Smyth P., Yu R., Slater S., Baker R., Warschauer M. Mining big data in education: Affordances and challenges. Review of Research in Education, 2020, no. 44 (1), pp. 130–160.
3. Ben Kei Daniel. Big Data and Learning Analytics in Higher Education: Current Theory and Practice. Springer, 2016. 272 p.
4. Ferriter W. M. Digitally Speaking. Positive Digital Footprints. Educational Leadership, 2011, no. 68 (7), pp. 92–93.
5. The digital footprint: new challenges for the education system in the Data era. URL: https://habr.com/ru/post/513616 (accessed 21 May 2022).
6. Mobasher G., Shawish A., Ibrahim O. Educational data mining rule based recommender systems. CSEDU (1), 2017, pp. 292–299.
7. 20.35 University. URL: https://2035.university (accessed 21 May 2022).
8. Bakumenko O. Elektronnaya internatsionalizatsiya i nauchnyye brendy universitetov [Electronic internationalization and scientific brands of universities] (in Russian). URL: https://russiancouncil.ru/analytics-and-comments/analytics/nuzhna-li-rossiyskim-universitetam-elektronnaya-internatsionalizatsiya-nauchnoy-deyatelnosti (accessed 21 May 2022).
9. Akimova O. B., Tcherbin M. D. Tsifrovaya transformatsia obrazovania: svoyevremennost’ uchebno-poznavatelnoy samostoyatel’nosti obuchayushikhsya [Digital transformation of education: timeliness of educational and cognitive independence of students]. Innovatsionnyye proyekty i programmy v obrazovanii – Innovative projects and programs in education, 2018, no. 1, pp. 27–34 (in Russian).
10. Kramarenko N. S., Kvashin A. Yu. Psikhologicheskiye i organizatsionnyye aspekty vvedeniya tsifrovogo obrazovaniya, ili Kak vnedreniye innovatsii ne prevratit’ v “tsifrovoy kolkhoz” [Psychological and organizational aspects of the introduction of digital education, or how the introduction of innovations cannot be turned into a “digital collective farm”]. Vestnik MGOU – Bulletin of Moscow Region State University, 2017, no. 4, pp. 1–16 (in Russian).
11. Aver’yanov A. O., Gurtov V. A., Semenov D. N., Kruglov V. I. Razvitiye eksporta rossiyskogo obrazovaniya: oriyentatsiya na potrebnost’ natsional’nykh rynkov truda [Export development of Russian education: focus on the needs of national labor markets]. Vyssheye obrazovaniye v Rossii – Higher Education in Russia, 2021, vol. 30, no. 4, pp. 9–21 (in Russian).
12. Prioritetnyy proyekt “Razvitiye eksportnogo potentsiala rossiyskoy sistemy obrazovaniya” [Priority project “Development of the export potential of the Russian education system”] (in Russian). URL: http://government.ru/projects/selection/653 (accessed 21 May 2022).
13. Buniyamin N., bin Mat U. B., Arshad P. M. Educational data mining for prediction and classification of engineering students achievement. 2015 IEEE 7th International Conference on Engineering Education (ICEED). IEEE, 2015. Рp. 49–53.
14. Analitika bol’shikh dannykh i Machine Learning v obrazovanii: 5 keysov iz vuzov [Big data analytics and Machine Learning in education: 5 cases from universities] (in Russian). URL: https://www.bigdataschool.ru/blog/big-dataanalytics-education-cases.html (accessed 21 May 2022).
15. Virtual’naya obrazovatel’naya sreda MGYuA [Virtual educational environment of Moscow State Law Academy] (in Russian). URL: https://sdo.msal.ru/admin/tool/dataprivacy/summary.php?lang=ru (accessed 21 May 2022).
16. Bogacheva N. V., Sivak E. V. Sovremennaya analitika obrazovaniya [Modern analytics of education]. Mify o pokolenii Z [Myths about Generation Z]. Moscow, HSE Publ., 2019. 64 p. (in Russian)
17. Saymon Kh. Neyronnyye seti: polnyy kurs. Perevod s angliyskogo [Neural networks: Full course]. Translated from English. Moscow,Williams Publ., 2008. 1103 p. (in Russian).
18. Balyakin A. A., Nurakhov N. N., Nurbina M. V. Digital Twins vs Digital Trace in Megascience Projects. Information Technology and Systems, 2021. AISC 1330. Р. 534–539.
19. Balyakin A. A., Malyshev A. S. Upravleniye bol’shimi dannymi v issledovatel’skikh infrastrukturakh [Big data management in research infrastructures]. Otkrytyye sistemy – Open Systems, 2020, no. 3, pp. 33–35 (in Russian). URL: https://www.osp.ru/os/2020/03/13055606 (accessed 21 May 2022).
20. Balyakin A. A., Nurbina M. V., Taranenko S. B. Ethics in Big Data: Myth or Reality. In: Á. Rocha et al. (eds.) Information Technology and Systems, 2021, AISC 1330, pp. 14–22.
21. Birhane A. Algorithmic injustice: a relational ethics approach. Perspective, 2021, patterns 2, vol. 2 (2), 100205, February 12.
22. Shkola tsifrovogo veka [School of the digital age] (in Russian). URL: https://www.hse.ru/twelve/part2 (accessed 21 May 2022).
23. Zhulego V. G., Balyakin A. A., Nurbina M. V., Taranenko S. B. Tsifrovizatsia obschestva: novyye vyzovy v sotsial’noy sfere [Digitalization of society: new challenges in the social sphere]. Vestnik Altayskoy akademii ekonomiki i prava – Bulletin of the Altay Academy of Economics and Law, 2019, no. 9-2, pp. 36–43 (in Russian).
24. Grekova A. A. Osobennosti myshleniya predstaviteley “tsifrovogo pokoleniya” [Features of thinking of representatives of the “digital generation”]. Vestnik YuUrGU – Bulletin of South Ural State University. Series: Psychology, 2019, vol. 12, no. 1, pp. 28–38 (in Russian).
25. Chastno-gosudarstvennoye partnerstvo. Uchiteley i chinovnikov perevedut na rossiyskiye messendzhery [Chatpublic partnership. Teachers and officials will be transferred to Russian messengers]. Kommersant, August 10, 2021. URL: https://www.kommersant.ru/doc/4936094 (accessed 21 May 2022).
26. Big brother brands report: which companies might access our personal data the most? URL: https://clario.co/blog/which-company-uses-most-data (accessed 21 May 2022).
27. Tsifrovoy sled: novyye zadachi sistemy obrazovaniya v epokhu dannykh [Digital footprint: new challenges for the education system in the age of data] (in Russian). URL: https://habr.com/ru/post/513616 (accessed 21 May 2022).
28. Geymifikatsiya v obrazovanii: vidy, komponenty, primery [Gamification in education: types, components, examples] (in Russian). URL: https://vuz24.ru/news/fakty-i-sobytiya/gejmifikaciya-v-obrazovanii-vidy-komponentyprimery (accessed 21 May 2022).
29. Virtual Reality A Big Part Of Dallas ISD’s New ‘Hybrid’ School. URL: https://dfw.cbslocal.com/2021/08/23/virtual-reality-big-part-dallas-isd-new-hybrid-school/ (accessed 21 May 2022).
30. Nurbina M. V., Nurakhov N. N., Balyakin A. A., Tsvetus N. Yu. Mega Science Projects for Business. In: T. Ahram et al. (Eds.) IHIET, 2020, AISC, 2021. Рp. 488–492.
31. Harari Y. N. 21 Lessons for the 21st Century. Vintage Digital, 2018. 416 p.
32. Kramarenko N. S. Samoosuschestvleniye cheloveka v usloviyakh real’nogo i virtual’nogo mira: sub’ektivny podkhod. Dis. kand. ped. nauk [Self-realization of a person in the conditions of the real and virtual world: a subjective approach. Diss. cand. ped. sci.]. Moscow, 2014. 314 p. (in Russian).
33. Vinuesa R., Azizpour H., Leite I. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 2020, no. 11, 233 p.
34. Saprykina A. Tsifrovizatsiya sverkhu vniz [Digitalization from top to bottom] (in Russain). URL: https://www.comnews.ru/content/208353/2020-07-30/2020-w31/cifrovizaciya-sverkhu-vniz (accessed 21 May 2022).
35. Kai-Fu Lee. Sverkhderzhavy iskusstvennogo intellekta: Kitay, Kremniyevaya dolina i novy mirovoy poryadok [The superpowers of artificial intelligence. China, Silicon Valley and the New World Order]. Moscow, Mann, Ivanov & Ferber Publ., 2019. 240 p. (in Russain).
36. Balyakin A. A., Mamonov M. V., Nurbina M. V., Taranenko S. B. Digital Footprint and Education: Some Remarks. In: Mesquita A., Abreu A., Carvalho J. V. (eds.) Perspectives and Trends in Education and Technology. Smart Innovation, Systems and Technologies. Springer, Singapore. Vol. 256. Рp. 485–493. https://doi.org/10.1007/978-981-16-5063-5_40
( 407.49 kB ) 
( 398.61 kB )
Issue: 5, 2022
Series of issue: Issue 5
Rubric: PROBLEMS OF MODERN EDUCATION
Pages: 9 — 19
Downloads: 475