The Yaroslavl Demidov State University (Russian: Ярославский государственный университет имени П. Г. Демидова) is an institution of higher education in Yaroslavl, Russia. In 1918, Yaroslavl Demidov State University became a successor university to the Demidov Lyceum, which was founded in 1803.
Novel cyano-containing 6H,14H-6,14-methanobenzo[g]benzo[4,5]thiazolo[2,3-d][1,3,5]-oxadiazocines were synthesized with a yield of up to 58
At the new stage of life, students will have to make independent decisions, starting with choosing a future profession, educational institution, and the amount of necessary knowledge, and procrastination can become a negative factor that will affect students in the process of educational and professional activities, disrupt self-regulation and slow down the formation of an effective individual style of activity. The theoretical analysis shows the importance of the formation of an individual style for the development of motivation, thinking, scientific and methodological competencies throughout life, contributes to success in the labor market. An empirical study conducted among college and university students showed significant differences in the studied indicators among students with different levels of procrastination from educational institutions of different levels. It is shown that, in general, individual styles of activity and methods of self-regulation used by university students are more effective. The result obtained is explained by the specifics of educational standards at the levels of secondary and higher education, as well as the peculiarities of the educational environment, which in colleges is characterized by a greater orientation towards education.
Abstract. The rapid integration of artificial intelligence technologies into mathematics education necessitates a scientific justification for their use in designing personalized learning paths. This paper aims to assess the effectiveness of different AI tools within a personalized digital learning environment for mathematics. The research design employed a comparative study in a natural educational setting, using questionnaires, analysis of students' digital footprints, statistical processing (Student's t-test, ANOVA, Tukey's test, Cohen's d), and thematic analysis of open-ended responses. The study involved 168 students working with 32 interactive mathematical modules on the "Academic Resilience" platform. The findings indicate that the effectiveness of AI tools is contingent on the type of mathematical content. The most significant pedagogical effect was observed for a combination of graphical environments and AI assistants in tasks requiring both visualization and analytical reasoning. For algorithmic topics, a dialog-based AI assistant proved to be the most effective. These results support the consideration of AI technologies as a tool for adaptive modeling of individual educational trajectories in mathematics education.
A new one-pot synthesis of 2-[N-(benzothiazol-2-yl)amino]-2H-chromenes involves the reaction of pyrimidine-2-thiones with 4-bromo-5-nitrophthalonitrile in the presence of DBU. The starting tricyclic reactants were obtained via the Biginelli reaction from salicylaldehydes, 1,3-dicarbonyl compounds, and thiourea.
Introduction. The relevance of this study is driven by the growing global gap between the content of higher professional education and the actual needs of the economy amid rapid technological, social, and institutional changes, which calls for new models of organizing education. The aim of the research is to identify common features and nationally specific characteristics in the organization of innovative higher-education ecosystems in China and Russia, and to outline the factors that determine the differences between these ecosystems. Materials and Methods. The study was carried out using a comparative-pedagogical approach and a content analysis of three groups of sources: the legal and regulatory acts of the two countries; official materials from ministries; and media publications reflecting the practice of university transformations. Results. The results reveal common features (the determining role of the state, integration of education, science, and industry) and significant differences in the university ecosystem model in each country. The Chinese model is characterized by centralized strategic planning, mass coverage, a developed digital infrastructure, and a rigid system of outcome assessment. The Russian model is distinguished by its flexibility, an emphasis on technological entrepreneurship and regional development, alongside fragmented digitalization. Conclusion. The experiences of both countries reveal effective, albeit methodologically different, approaches to constructing university ecosystems – which diverge in particular mechanisms, principles, and the role of digitalization – along with divergent outcomes and varying magnitudes of eff ort. The practical significance of the work lies in the possibility of directly applying its findings to optimize the management of universities that are building ecosystems and to develop educational programmes. A promising direction for further research is an in-depth analysis of the mechanisms of sustainable partnership and overcoming regional disparities in each country, taking into account global educational trends and national development strategies, as well as an assessment of the long term effects of ecosystem transformation.
