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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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The Interplay of Self-Regulated Learning, Cognitive Load, and Performance in Learner-Controlled Environments

Education Sciences. 2024. Vol. 14. No. 8. Article 860.
Gorbunova A., Lange C., Savelyev A., Adamovich K., Costley J.

Learner control allows for greater autonomy and is supposed to benefit learning motivation, but it might be more advantageous for students with specific learner characteristics. The current study looks into the relationships between self-regulated learning, cognitive load, and performance within learner-controlled environments. The research was conducted in an asynchronous online setting, allowing for learner control. Cognitive load and self-regulated learning were measured using self-report questionnaires. Performance was assessed through case solutions. The participants were 97 graduate law students studying the civil code. Analysis based on structural equation modeling showed that both prior knowledge and self-regulated learning skills significantly contribute to the increase in germane cognitive load and are positively correlated with performance. The implications of these findings underscore the critical role of prior knowledge and self-regulated learning skills in shaping the cognitive processes involved in learning, ultimately impacting academic achievement. These results emphasize the need for careful consideration of learner-control options in asynchronous online environments.

Research target: Education Law
Language: English
DOI
Keywords: academic performanceself-regulated learninglearner controlprior knowledgecognitive load theory
Publication based on the results of:
The relationship between self-regulated learning skills, motivation and engagement in the context of learning on online platforms (2024)
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