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News
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Tracking Self-Regulated Learning Strategies in Blended Learning: A Digital Trace Approach

Online Learning Journal. 2026. Vol. 30. No. 1. P. 265–293.
Boitcov M., Gorbunova A., Kapuza A., Sutarmina R.

This study examined digital traces in the form of clicks on Learning Management System (LMS)
modules to infer and monitor students' Self-Regulated Learning (SRL) strategies in a blended
learning environment. Digital traces were aligned with students' responses on the A-SRL
questionnaire (Magno, 2010), and these findings were further triangulated through student
interviews. The results suggest that clicks on LMS modules containing theoretical material and
gradebook indicate students’ utilization of a single corresponding SRL strategy and may serve as
a good indicator of SRL behavior, while other digital traces relate to multiple strategies at once
and lack a singular interpretation. Additionally, it was found that students in blended courses
may utilize SRL strategies outside of the LMS. The study offers practical implications for
instructional designers and practitioners seeking to support SRL in blended learning
environments.

Research target: Education
Language: English
Full text
DOI
Text on another site
Keywords: blended learningself-regulated learningdigital traces
Publication based on the results of:
Using digital traces to study learners' behavioral strategies (2025)
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