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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Вовлеченность обучающихся массовых открытых онлайн-курсов по продуктам вендора облачных технологий

Информатика и образование. 2025. Т. 40. № 2. С. 57–65.
Porosenkov G., Цепелевич М. М., Кизяков Д. А., Селезнева В. Н.

Research on learner engagement in massive open online courses (MOOCs) has predominantly focused on those developed by educational and non-profit organizations. However, the significance of MOOCs offered by IT service providers (vendors), particularly in the field of cloud computing, has gained prominence in recent years. These vendor-led courses facilitate interaction between learners and vendors, underscoring the necessity of investigating engagement levels within MOOCs from cloud service providers. The aim of the study presented in this article is twofold: first, to identify learner types based on their engagement patterns while participating in the “Cloud Services Engineer” MOOC developed by Yandex Cloud; second, to ascertain the factors influencing learner engagement in this context.
To achieve the aims, learners enrolled in the “Cloud Services Engineer” MOOC were clustered based on their level of engagement, which was assessed using data on lesson completion and test performance. Furthermore, the study examined the relationship between cluster membership and factors such as prior experience in cloud computing, professional position, and motivations for enrolling in the course.
The analysis revealed three distinct clusters of learners. The first cluster comprises highly engaged individuals who actively strive to obtain a certificate, likely motivated by institutional or employer requirements. The second cluster includes learners demonstrating prolonged interaction with educational materials but limited test attempts, suggesting intrinsic motivation to master cloud skills without the necessity for formal ascertainment of knowledge; and the third cluster consists of learners with minimal engagement, focusing on specific topics within the MOOC without seeking formal validation of their knowledge.
The findings highlight the importance of designing non-linear MOOCs that cater to diverse learner needs and interests in cloud learning.

Research target: Economics and Management
Language: Russian
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Text on another site
Keywords: мотивацияcluster analysisкластерный анализmotivationоблачные технологииMOOC cloud computingМООКмассовый открытый онлайн-курсstudents’ engagementMassive open online coursetraining provided by vendorsобучение от вендороввовлеченность обучающихся
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