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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Maximising academic achievement in online learning: the role of student-content engagement strategies and profiles

Interactive Learning Environments. 2024. Vol. 33. No. 3. P. 2175–2187.
Getman A., Pekeleu M.

Learner engagement is crucial for success in online learning, and interaction plays a key role in developing engagement. While previous studies have focused on student-instructor and student-student interaction, this study examines the impact of student-content interaction, measured on digital traces data, on academic achievement in an online course. By considering specific engagement strategies and profiles, a more comprehensive understanding of student-content interaction is achieved. Three engagement profiles were identified: synchronously engaged, asynchronously engaged, and disengaged. The synchronously engaged profile was found to be the most beneficial for academic achievement, especially for high-performing students. The study also revealed a positive relationship between engagement strategies and academic achievement, with online webinar attendance being the most contributing. Additionally, the impact of engagement strategies and profiles varied for students with different levels of prior performance. Practical implications for enhancing and monitoring engagement among different student groups are discussed.

Research target: Education
Language: English
DOI
Text on another site
Keywords: digital tracesstudent engagementengagement profilesengagement strategiesstudent-content interaction
Publication based on the results of:
The relationship between self-regulated learning skills, motivation and engagement in the context of learning on online platforms (2024)
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