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News
September 21, 2026
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.
September 21, 2026
Algebra, Geometry, and AI: Russian and Vietnamese Mathematicians Discuss Current Research
A delegation of scientists from Hanoi visited the HSE Faculty of Computer Science and then took part in a Russian-Vietnamese conference in St Petersburg. The events were part of the three-year project ‘Flexibility and Computational Methods.’ Over the course of the project, the researchers have prepared joint publications and obtained new mathematical results.
September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.

 

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Исследовательский проект как инструмент обучения методам анализа текста: предсказание класса поста в социальной сети

Компьютерные инструменты в образовании. 2018. № 3. С. 49–64.
Suvorova A., Смирнова К. Р., Будин Е. А., Тулупьева Т. В., Тулупьев А. Л., Абрамов М. В.

The article describes a student research project on predicting the class of a post on a social network based on its textual content. The features of the project are discussed as an integral part of the trajectory of teaching data analysis methods, including text analysis methods and tools that are often not included in machine learning courses. The formulation of the problem, the stages of its solution, the sequence of considering new methods as a way for solving students’ problems, as well as the used tool of the R environment are described. The possibilities of expanding the task and its modi1cations depending on the level of training of students are given.

Priority areas: IT and mathematics
Language: Russian
Full text
DOI
Keywords: социальные сетимашинное обучениеsocial networksклассификацияанализ текстаtext analysismachine learningclassificationProblem-based learningR languageязык Rпроблемно-ориентированное обучениеresearch automationавтоматизация исследований
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