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June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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Робототехника в России: образовательный ландшафт

Ч. 2. M. : National Research University Higher School of Economics, 2019.
Gagarina D., Kosaretsky S., Гагарин А. С., Goshin M. E.
Academic editor: Kosaretsky S., Gagarina D.
Under the general editorship: Kosaretsky S., Gagarina D.

It also summarizes the results of many years of work included expert interviews, surveys, visits to organizations, competitions and conferences, the study of the regulatory framework, policy documents and educational programs. The collected materials cover all levels of education and a wide range of issues: the sector’s structure, state policy, contests and competitions, methodological and staffing, accessibility for different categories of children, etc. There is an evidence-based country assessment of the sector’s development, landscape and state policy changes, it has been also identified the development drivers and barriers and highlighted the features of the situation in different directions. The current study shows that robotics has become not only the subject of direct learning, but it is also a medium of instruction in teaching other disciplines and the tool for the high-order skills development, it strongly provides the children and adolescents’ motivation and career guidance including disables. At the same time, the sustainable models of content and staffing, coordination mechanisms have not been formed yet despite of the robotics leading positions as a sector of education development, its attracting public interest and involving various stakeholders. The book is published in two parts. The first one includes the conceptual framework, the sector’s history, its staffing and methodological support, competitions in robotics. The second part discusses the levels and directions of educational robotics. The publication is addressed to professional community of the educational robotics sector, managers and teachers of all education levels.

Research target: Education
Priority areas: IT and mathematics engineering science
Language: Russian
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Keywords: инженерное образованиеробототехникаroboticsSTEMSTEMeducational roboticsобразовательная робототехника engineering education
Робототехника в России: образовательный ландшафт
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