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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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Механизмы содержательной обратной связи как элемент управления качеством проектного обучения в вузе

Университетское управление: практика и анализ. 2025. Т. 29. № 4. С. 97–111.
Kutuzov A., Богданова А. В., Patarakin Y.

Abstract. As project-based learning becomes increasingly integrated into higher education, the demand for new tools to monitor collaborative work has grown significantly. This article presents an approach to generating feedback based on the analysis of digital traces produced within professional collaboration tools – specifically task trackers (e.g., Trello, Wekan) and code repositories (e.g., GitLab). The study reviews current frameworks for feedback provision and digital trace analysis, identifies the sources and structure of digital traces in project-based work, and proposes a method for extracting and processing student activity logs. Social Network Analysis (SNA) techniques are applied to assess the interaction structure within teams, and an interpretive model is introduced to generate feedback based on network metrics. The empirical basis of the study consists of 253 student projects implemented between 2019 and 2023 at MIEM, National Research University Higher School of Economics. The findings demonstrate that visualizing network characteristics and role dynamics within teams significantly enhances the ability to detect key interaction patterns and atypical behaviors that may require pedagogical intervention. Integrating such mechanisms into digital learning environments offers a unique opportunity for continuous monitoring of team processes and for improving the quality management of project-based learning.

Research target: Education
Language: Russian
DOI
Text on another site
Keywords: feedbackпроектное обучениеproject-based learningцифровые следыteamworkучебная аналитикакомандная работаdigital footprints learning analyticsобратная связь в обучении
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