Institute of Education

Publications

• Book

  Abashkin V., Abdrakhmanova G., Vishnevskiy K. et al.

  Digital Economy: 2025 : Pocket Data Book

  This pocket data book contains the main indicators about the digital economy development.

  The data book includes information of the Russian Federal State Statistics Service (Rosstat), Ministry of Digital Development, Communications and Mass Media of the Russian Federation, European Statistical Office (Eurostat), Organisation for Economic Co-operation and Development (OECD), International Telecommunication Union (ITU), United Nations Conference on Trade and Development (UNCTAD), UN Department of Economic and Social Affairs, World Intellectual Property Organization (WIPO), and results of methodological and analytical studies of the HSE Institute for Statistical Studies and Economics of Knowledge.

  In some cases, the presented data specify those published earlier.

  M.: HSE, 2025.

• Article

  Mikhaylova O., Zvyagina S.

  Comments on YouTube videos as a source of advice for supporting child autonomy: interpreting autonomy as independence, reflexivity, nature/nurture, and nostalgia for the USSR

  YouTube represents a vast resource that parents may consult for advice; however, little is known about the information available to parents seeking advice on supporting their children’s autonomy. This study aimed to analyze the comments (of parents) responding Russian-language YouTube videos focused on advice for child autonomy support. Following a descriptive cross-sectional design, YouTube was searched using the phrase ‘child autonomy.’ Qualitative content analysis was used to assess comments from videos that met the inclusion criteria. The analysis revealed that viewers interacted with the content of videos about parental autonomy support in children. Specifically, they expressed reflexivity toward the advice given. However,  their discussions focused on only one type of autonomy—independence—while almost completely neglecting tactics that would support volitional functioning. Additionally, the commenters tended to shift the responsibility for parenting to external entities, arguing that raising autonomous children had been easier in the USSR of the past. While this may be attributed to nostalgia- it was also apparent that they shifted the locus of blame also to  include  the educational system (4), fathers that gave a bad example (2), and the current economic conditions that permitted the child to be financially independent (1) although it was not clear how they viewed these as constraints. Recommendations based on the findings include encouraging professionals involved in child development to film and release more qualified content on YouTube for peer review, supporting autonomy in children on YouTube by raising the quality of the contents and suggesting that this could be done by working with professionals as well as government agencies participating in making helpful videos.

  Global Studies of Childhood. 2025.

• Book chapter

  Maloshonok N., Zhuchkova S., Bekova S. et al.

  The quality of admissions to Russian doctoral programmes during the COVID-19 pandemic

  Previous studies have shown that the COVID-19 pandemic had a negative impact on higher education systems and student learning globally. However, despite this, many countries experienced an increase in doctoral enrolment in 2020, which has raised concerns about the quality of admission and the motivation of doctoral candidates during the pandemic. This article aims to explore this context by delving into statistics about Russian doctoral programmes, which saw a decline in enrolment from 2010 to 2019. We use data from a web survey of 1,895 students enrolled in doctoral programmes at Russian universities in 2020. The results suggest that the increase in enrolment came in tandem with a decrease in the quality of doctoral admission. More students with non-academic motivation and a lack of academic skills and attainment were admitted. This situation is likely to prompt completion rates to lower over the course of the next three to five years.

  In bk.: Global Perspectives on Graduate and Doctoral Education: International Case Studies. L.: Routledge, 2025. P. 103-112.

• Working paper

  Kardanova E., Ivanova A., Tarasova K. et al.

  A Novel Psychometrics-Based Approach to Developing Professional Competency Benchmark for Large Language Models

  The era of large language models (LLM) raises questions not only about how to train models, but also about how to evaluate them. Despite numerous existing benchmarks, insufficient attention is often given to creating assessments that test LLMs in a valid and reliable manner. To address this challenge, we accommodate the Evidence-centered design (ECD) methodology and propose a comprehensive approach to benchmark development based on rigorous psychometric principles. In this paper, we have made the first attempt to illustrate this approach by creating a new benchmark in the field of pedagogy and education, highlighting the limitations of existing benchmark development approach and taking into account the development of LLMs. We conclude that a new approach to benchmarking is required to match the growing complexity of AI applications in the educational context. We construct a novel benchmark guided by the Bloom's taxonomy and rigorously designed by a consortium of education experts trained in test development. Thus the current benchmark provides an academically robust and practical assessment tool tailored for LLMs, rather than human participants. Tested empirically on the GPT model in the Russian language, it evaluates model performance across varied task complexities, revealing critical gaps in current LLM capabilities. Our results indicate that while generative AI tools hold significant promise for education - potentially supporting tasks such as personalized tutoring, real-time feedback, and multilingual learning - their reliability as autonomous teachers' assistants right now remain rather limited, particularly in tasks requiring deeper cognitive engagement.

  Computation and Language (cs.CL); Artificial Intelligence (cs.AI). cs.CL. arXiv, 2024