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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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Как аукнется, так и откликнется? Отношение студентов-лингвистов к взаимооцениванию и обратной связи

Вестник Томского государственного университета. 2023. № 487. С. 118–128.
Bogolepova S., Kirsanova M., Пивоварова А. А.

This study is aimed at determining the attitude of Russian university students of linguistics to peer-assessment and peer-feedback, as well as their general level of feedback literacy. The data were collected through an electronic survey based on the framework proposed by Molloy, Boud & Henderson (2020). The participants of the survey were 137 third-year bachelor students of Higher School of Economics who study linguistics and have experience of giving feedback on their peers’ writing in English. The survey consisted of both close-ended items (statements to evaluate on the Likert scale) and open-ended items, in which the participants were asked to describe the challenges they encounter and the feelings they have when giving peer-feedback online. The data collected from close-ended questions in the survey were then subjected to descriptive and cluster analyses, and the responses to open-ended questions were thematically analysed. The data analysis has led to several inferences. Firstly, the results have shown that students’ attitudes to peer-feedback are mixed. Though the students are aware of the purpose of peer-assessment and feedback, they are not sure their feedback motivates or allows their peers to improve their works, showing a preference for feedback received from an instructor. Although two clusters with opposite attitudes to peer-feedback can be identified, overall, Russian university students do not fully appreciate and recognise the value of peer-feedback and their own active role in the feedback-giving and feedback-receiving process. As for how Russian students engage with peer-feedback they receive, the majority merely read through feedback without making any corrections or improvements to their work. Secondly, the participants demonstrated a strong preference for giving peer-feedback online, as the online environment allows for more time to analyse a peer’s paper and to draft a commentary. Other preferences include the presence of a rubric and explication of errors. Thirdly, feedback-giving is a challenge for the students because they feel that they lack the necessary qualifications and competence to do it or they are afraid of offending their peers. The participants often struggle to identify and correct language mistakes and to formulate precise feedback comments. These challenges and preferences may be influenced by the collectivistic nature of Russian culture and its high power distance indicator. It can be inferred that the feedback literacy of Russian university students is generally low and is in need of purposeful improvement.

Research target: Education Philology and Linguistics
Language: Russian
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Text on another site
Keywords: обратная связьfeedbackвзаимооценкаpeer-assessmentвзаимооцениваниеpeer-feedbackfeedback literacyfeedback engagementграмотность в вопросе обратной связивзаимодействие с обратной связью
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