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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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The network approach to assess the structure of knowledge: Storage, distribution and retrieval as three measures in analysing concept maps

British Journal of Educational Technology. 2020. Vol. 51. No. 6. P. 2573–2590.
Kapuza A., Koponen I., Tyumeneva Y. A.

We present three new standardised network concept map (CM) measures that can provide unique information about learning‐related progress, which cannot be determined from previously known measures. Grounded in cognitive development theory on the one hand, and network theory on the other hand, our measures reveal how knowledge is stored, distributed and retrieved. We validated the new measures by testing their ability to discriminate between CMs of respondents with different levels of competency in statistics (students before and after taking an introductory statistics course and experts in the field of statistics). We also validated our measures against the most commonly used traditional and network measures. Based on a small sample of respondents, we show that two of the newly proposed compound measures reveal significant differences between experts and novices in the field, with higher values for experts, showing that expert knowledge is better distributed, more connected and balanced. More importantly, our measures were sensitive enough to show learning‐related progress for students, albeit statistically non‐significant, while common indicators from network theory did not demonstrate these small shifts. The validity of our new measures can be inferred from the consistency of the results from different sets of measures.

Research target: Psychology Education
Priority areas: IT and mathematics
Language: English
DOI
Text on another site
Keywords: обучениеlearningexpertsэкспертыnetwork analysisknowledge structureСетевой анализ в образованииконцептуальные картыструктура знанийconcept mapsnovices
Publication based on the results of:
Strategies, barriers and factors for including digital technologies in the educational process by primary school teachers (2020)
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