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News
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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Development of a self-regulated learning scenario scale with holistic and specific answer options

Quality and Quantity. 2026. P. 1–24.
Antipkina I., Kavalchuk I., Ludlow L.

In this study, we (1) test whether a single vignette item can be used as a stimulus for several questions referring to different vignette aspects; (2) explore the development of vignette items so that they could be comprehended holistically. The Rasch-Guttman Scenario Scale (RGSS) methodology was used to develop a questionnaire on self-regulated learning (SRL). 12 vignettes depicted high/medium/low SRL levels across three of the six SRL components (Selecting Main Ideas; Test Strategies; Concentration; Motivation; Attitude to Learning; Time Management). Cognitive interviews confirmed respondents’ ability to distinguish facets within a vignette. However, in a sample of 528 students, only the holistic questions aimed at the whole vignette formed a reliable, coherent Rasch scale; facet-specific items failed to yield valid subscales. A replication with>2,000 students using only holistic items confirmed reliability, similar item hierarchy, and expected correlations with motivation and diligence scales. We conclude that a vignette-based instrument is promising for holistic measurement of SRL-related behaviours and concepts. Extracting fine-grained facet scores from a single vignette was psychometrically unsupported in large-scale assessment.

Research target: Education
Language: English
DOI
Text on another site
Keywords: measurementself-regulated learningvignettescenario scalesRasch/ Guttman Scenario scales
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