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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Интеграция школ в Латвии и Эстонии через реформу содержания образования

Вопросы образования. 2019. № 3. С. 77–100.
Khavenson T.

This article investigates into the reform of national school curriculum in Russian-language schools in Latvia and Estonia. We assess how well the reform-related regulations have been integrated into everyday schooling practices and reflected in educational outcomes in order to measure the success of the education reform in terms of adopting the new learning standard and improving the PISA results. The study exploits the situation of natural experiment that followed the collapse of the Soviet Union, with countries that used to have a common education system taking different reform paths and achieving different outcomes. National school curriculum is analyzed at three levels: as intended (stipulated in documents), as implemented (taught by school teachers), and as attained (reflected in test results). Such three-level analysis required studying the documents that described the key reform provisions, conducting a series of in-depth interviews in Russian-language schools to investigate the process of integrating the proposed innovations in teaching practices, and analyzing how PISA results in Latvia and Estonia had changed between 2006 and 2015. It is shown that the gap between the curriculum as intended and as attained has reduced in both countries. Schools have been actively integrating the changes proposed, and PISA results have been improving consistently, yet the methods of achieving those results differ between the countries. The natural experiment study design allowed to explore educational reform processes in the two countries as well as to assess the effects of the reforms introduced.

Research target: Economics and Management Sociology (including Demography and Anthropology Education
Priority areas: sociology
Language: Russian
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Keywords: PISAпостсоциалистические страныcomparative educational research государственный образовательный стандартPISAfederal curriculumсравнительные исследования в образованииpostsocialist countriesanalysis of reform methodologyschool curriculum reformРеформа школьного образованияМетодология анализа реформ
Publication based on the results of:
Innovative behavior as a factor of sustainable society development: characteristics of actors, ecosystem and the mechanisms of support through education system (2019)
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