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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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Особенности восприятия офлайн и онлайн учебного опыта студентами

Отечественная и зарубежная педагогика. 2025. Т. 1. № 6(109). С. 73–83.
Kuryan M., Voronina Y.

Relevance: The paper looks into how students perceive their learning experiences acquired in traditional in-person and online formats. The relevance of this research is substantial, given the context of digital transformation and personalization of higher education. The aim of the study is to analyse student characteristics - gender, year of study and educational profile - that may influence their preferences for either of the educational formats. Methods: The study, conducted in the form of a survey in 2024, involved undergraduate students at the HSE University in Nizhny Novgorod. The sample was stratified by gender, year of study, and academic major. The analysis focused on comparing parameters of learning experience, such as motivation, self-discipline, the social component of the learning environment, and students’ perceptions of course structure, materials, assignments, and assessment systems. Results: Female students rated socialization and interaction higher, especially in the offline format. Gender differences in the perception of student-teacher communication and course content were minimal, but women showed higher self-regulation. First-year students had the highest motivation. Economics majors were more positive towards both learning formats than their peers studying management and computer science. Conclusions: Students' preferences depend on many factors. The research results emphasize the need for individualized educational trajectories and flexible course design which take into account the characteristics of the learners.

Research target: Education Psychology
Language: Russian
DOI
Text on another site
Keywords: образовательная средаonline learningонлайн-обучениеlearning environmentstudents' characteristicsиндивидуализация образованияindividualization of educationoffline learningофлайн обучениеstudents' preferencesхарактеристики студентовпредпочтения студентов
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