Assessment of M&amp;A strategy: the case of YANDEX company

Ivankova, Galina Vladimirovna; Mochalina, E. P.; Dubolazova, Yulia; Zaytcev, Dmitry; Korenev, Maxim

doi:10.1145/3527049.3527065

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…There was a decrease in 2021 by 70% to 233 works. The main research directions were sociology (1,059 publications), business (1,044), knowledge management (725), and computer science (608). Pedagogy lags behind the leaders (362 publications).…”

Section: Introductionmentioning

confidence: 99%

“…The relevance of the need for pedagogical rethinking and the creation of realistic models for the formation of digital educational ecosystems is justified by the importance of resolving the modern contradiction: on the one hand, there is fragmentary positive practical experience in the development of digital ecosystems [1]in sectors other than education [2] (Meta, Alibaba, Amazon, Walmart, Ozon, Apple, Google, Microsoft, "Yandex," Mail.ru, Sber, Facebook, Tinkoff, Avito, and others), and we are actively involved in them [3]. There is a need within the pedagogical community to identify and utilize the pedagogical potential of these technologies, as voiced by the international scientific and pedagogical community [4].…”

Section: Introductionmentioning

confidence: 99%

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Digital ecosystem for enhancing research and innovation activities of master's students

Chvanova

2024

P Sci Edu

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Introduction. The dynamic development of modern digital technologies emphasizes the need for the development of new tools to activate the research and innovation activities of master's students. A digital ecosystem holds significant pedagogical potential not only in the academic activities of students but also in research and innovation. However, its creation is complicated by a lack of understanding of the essence, structure, and emergent properties of the system. The purpose of the study is to identify the features, mechanisms, and functionality of the digital educational ecosystem to enhance the research and innovation activities of undergraduates. Materials and methods. Bibliographic databases such as Scopus, Web of Science, Lens, and other international information systems of peer-reviewed scientific literature were used for the analysis of publications. System analysis and analysis of internet resources were employed to address the research objectives. The results of the study. The functional capabilities of the digital educational ecosystem for activating the research and innovation activities of master's students were identified, including access to information systems and databases of scientific information, automation of research management, creation and administration of electronic surveys, access to tools for processing and analyzing large volumes of data, access to data visualization tools, collaboration systems for working on research projects, automation of planning, tracking, and organizing research or educational projects, access to electronic systems for applying for research grants and scholarships, access to specialized plagiarism detection systems, electronic portfolios, access to publication management systems, automation of the creation and formatting of scientific articles and presentations, automation of publication formatting, access to virtual laboratories and modeling environments. Conclusion. The presented analysis allowed for the identification and justification of the pedagogical potential of the digital ecosystem for the core research and project activities of master's students, specifically: providing research with information resources; improving data collection and analysis; optimizing collaborative and network interactions; automating project management; digitally supporting the research process; digital technologies for stimulating creativity; digital support for publication preparation; expanding research boundaries; self-development; supporting distance learning; supporting virtual laboratories and modeling environments; ensuring data visualization; instrumental support for research quality analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%