Established in 2012, the Institute of Education (IOE) is one of the key R&D units at the National Research University Higher School of Economics, the leader of the QS Rankins in Education Russia.
At IOE, we research, train, and network to craft a better world through better education. Our supreme commitment is to contribute to robust, evidence-centric policy and practice so everyone benefits from positive change in education and development.
We boast world-class expertise brought by 250+ research and teaching faculty, including academics of international renown, who have diverse backgrounds and are into various scholarly strands.
Our R&D portfolio comprises a vast range of projects—including high-caliber partnerships with QS top-rank institutions and global policy powerhouses—that cut across educational realms.
May to August
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A leading global venue to share the latest and most relevant research insights into higher education and beyond
A series of weekly seminars that foregrounds topics central to modern educational research, policy, and practice
Science. Technology. Innovation: 2024 : Pocket Data Book
This pocket data book contains main S&T and innovation indicators for the Russian Federation. The publication includes the most recent statistical data on R&D input and output, as well as international comparisons. The data book includes information of the Russian Federal State Statistics Service (Rosstat), Ministry of Science and Higher Education of the Russian Federation, Russian Federal Service for Intellectual Property (Rospatent), Organisation for Economic Co-operation and Development (OECD), European Statistical Office (Eurostat), UNESCO, World Intellectual Property Organisation (WIPO), national statistical offices of other countries, and results of methodological and analytical studies of the HSE Institute for Statistical Studies and Economics of Knowledge. In some cases, 2022 data are preliminary.
National Research University Higher School of Economics, 2024.
Are Inductive Teaching Methods Compatible with Cognitive Load Theory?
Since Bruner’s introduction of discovery learning in the 1960s, there has been an ongoing and intensive debate on the value of inductive teaching methods (e.g., problem-solving followed by instruction, such as in problem-based learning and productive failure) compared to deductive teaching methods (instruction followed by problem-solving, such as direct instruction). Although it has been strongly argued that problem-first inductive methods are incompatible with human cognitive architecture as perceived by cognitive load theory, the main goal of this position paper is to appeal to broaden cognitive load research on inductive and deductive methods, and especially, their orchestration in educational programs of longer duration. We describe eight possible sequences of problem-solving and instruction and conclude that, when well designed, at least six of these sequences can be compatible with cognitive load theory, including productive failure and problem-based learning. We suggest that rather than comparing inductive with deductive methods, future research should also include inductive methods that use different types of supported problem-solving in combination with expository and inquisitory instruction. We propagate a design perspective, looking for the instructional goals, learner characteristics, and other conditions that make selected teaching methods effective, efficient, and attractive.
Educational Psychology Review. 2023. Vol. 35.
Pandemic Lessons: Story of Cooperation and Competition in Russian Education
This chapter examines how main actors such as policymakers, school teams, and Edtech companies faced the pandemic challenges and whether they cooperated with each other. The analysis demonstrates that while before COVID-19, Russian schools and Edtechs rarely cooperated with each other, the partnership developed in response to the necessity of an emergency transition to distance learning. The government attempted to establish a nationwide infrastructure for distance learning and the vetting of educational content during the initial stages of the pandemic, however, this strategy was not implemented. Since the government did not immediately react to the situation, schools were forced to cope with the transition themselves. EdTech helped students, teachers, and regions deal with the crisis. After the pandemic, EdTech companies found themselves in a situation of increased government regulation, to which they reacted differently: some companies preferred to focus on B2C formats, while others responded with investments in the B2G sector. The school-Edtech partnership might be one of the most far-reaching positive changes of the pandemic for education, but our analysis shows this lesson has rather not been learned.
In bk.: Schools and Society During the COVID-19 Pandemic: How Education Systems Changed and the Road Ahead. Switzerland: Springer, 2024. Ch. 9. P. 169-192.
Factors Influencing Adolescent Alcohol Consumption: Parents And DepressionAlcohol use is a common form of risky consumption among adolescents. Little research has been carried out on the influence of such factors as parental control, relationships with parents, and teenage feelings of depression on the frequency of alcohol consumption among adolescents in Russia. In this paper, structural models were developed to describe the influence of these factors on adolescent alcohol consumption and the relationship between the factors. Alcohol consumption in adolescents is represented in the work in two ways: casual alcohol use and binge drinking (the consumption of four or more servings of alcohol at a time). The respondents were students at vocational schools who participated in a longitudinal project to study the risky behavior of adolescents in St. Petersburg. Four waves of the survey were used: 1, 5, 6 & 7. According to the results, the strongest direct negative effect on alcohol consumption is caused by parental monitoring. However, the direct influence of monitoring on adolescent alcohol consumption was significant in Wave 1. But in Wave 6, this influence was insignificant, which can partially be explained by the age of the respondents, most of whom were already adults at the moment of completing the questionnaire in Wave 6. Regarding the relationship with parents, no direct influence on alcohol consumption was detected—only an indirect effect mediated by parental monitoring. The positive correlation between the relationship with parents and the level of monitoring was significant in Waves 1 and 7. The level of depression in adolescents was a significant predictor of drinking behavior only in the model describing alcohol consumption as the frequency of casual drinking. In the models describing binge drinking, this relationship was insignificant. In all models, there was a stable negative relationship between the relationship with parents and depression in adolescents.
Sociology. SOC. Высшая школа экономики, 2023. No. 101.
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