Learning analytics in Russia and abroad: level of development, trends and prospects
- 作者: Kustitskaya T.A.1, Noskov M.V.1
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隶属关系:
- Siberian Federal University
- 期: 卷 20, 编号 2 (2023)
- 页面: 150-158
- 栏目: MANAGEMENT OF EDUCATIONAL INSTITUTIONS IN THE INFORMATION ERA
- URL: https://journal-vniispk.ru/2312-8631/article/view/321278
- DOI: https://doi.org/10.22363/2312-8631-2023-20-2-150-158
- EDN: https://elibrary.ru/KEROFN
- ID: 321278
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Problem statement. Learning analytics is an emerging scientific field, which studies learners and learning process based on data from digital environment. The aim of the study - to observe the development of learning analytics, its prospects and limitations and detecting the state of art of this scientific field in Russia. Methodology . The study is based on context analysis of scientific articles on the topic in the public domain. Special attention is given to reviewing scientific publications of Russian-speaking authors devoted to analytics of education data and the implementation of learning analytics tools in the educational process. Results . The research detects the global directions of learning analytics development and its problematic aspects. It provides the quantitative and qualitative analysis of scientific publications of Russian-speaking authors and identifiers the most popular research questions in the learning analytics field. It proposes the author’s vision of the hierarchy of directions for learning analytics development, consisting of the research aspect, the environment transformation aspect and the legal regulation aspect. The national initiatives in the digitalization of education are briefly discussed. Conclusion . A certain lag in the level of development of learning analytics in Russia from the global one is revealed. At the same time, there is a noticeable increase in interest to this area among individual researchers, educational institutions and at the state level, which allows us to count on positive changes.
作者简介
Tatiana Kustitskaya
Siberian Federal University
编辑信件的主要联系方式.
Email: tkustitskaya@sfu-kras.ru
ORCID iD: 0000-0001-9854-1259
SPIN 代码: 5202-8701
Candidate of Physical and Mathematical Sciences, Associate Professor, Associate Professor of the Department of Applied Mathematics and Computer Security
79 Svobodnyi Prospekt, Krasnoyarsk, 660041, Russian FederationMikhail Noskov
Siberian Federal University
Email: MNoskov@sfu-kras.ru
ORCID iD: 0000-0002-4514-7925
SPIN 代码: 3957-7221
Doctor of Physical and Mathematical Sciences, Professor, Professor of the Department of Applied Mathematics and Computer Security
79 Svobodnyi Prospekt, Krasnoyarsk, 660041, Russian Federation参考
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