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  • Метрики центральности сетевого анализа как инструмент определения уровня абстракции понятий внутри понятийной структуры обучающихся
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News
October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Метрики центральности сетевого анализа как инструмент определения уровня абстракции понятий внутри понятийной структуры обучающихся

Психологические исследования: электронный научный журнал. 2025. Т. 18. № 102. Статья 3.
Andronova E., Kapuza A.

This study examines concepts as systemic elements within a conceptual structure, drawing on the theoretical framework of L.S. Vygotsky’s cultural-historical psychology. A key characteristic of concepts is their level of abstraction, which reflects their degree of generalization within a system. However, existing classification methods lack a unified approach, complicating empirical research. The current study aimed to explore network analysis metrics as a tool for distinguishing concepts of different levels within a concept structure. We analysed 242 concepts from conceptual maps created by experts and students in data analysis. First, a qualitative analysis was conducted using formal definitions and expert evaluations. Then, betweenness centrality and degree centrality metrics were applied to assess their effectiveness in distinguishing conceptual levels. The findings showed that betweenness centrality and normalized betweenness centrality significantly differed across levels: higher-level concepts had higher values compared to the basic and subordinate ones. Degree centrality varied depending on the classification method. When classified by definition, all group differences were shown to be significant. When classified by maps, differences were found only for the subordinate level. However, the number of incoming connections did not show distinguishing capability. Based on the results, it can be assumed that network analysis demonstrates potential not only in assessing the systemic characteristics of a conceptual structure but also in evaluating the role of individual concepts within it.

Research target: Psychology Education
Language: Russian
DOI
Keywords: сетевой анализnetwork analysisconcept structureкультурно-историческая теорияконцептуальные картыconcept mapsL. Vygotsky’s cultural-historical theory levels of abstractionпонятийная структурауровни абстракции понятий
Publication based on the results of:
Using digital traces to study learners' behavioral strategies (2025)
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