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
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.
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.
The article examines the problem of training the future primary school teachers. The environment of higher professional education should meet the inquiry of society and the state as well as take into account the specif-ics of students’ inclinations in order to train highly qualified personnel. A future teacher’s communicative and organizational inclinations provide the mastery of professional tasks, the quality of interpersonal interaction, and optimal adaptation to professional activities. The purpose of our research was to study the dynamics of teachers’ communicative and organizational inclinations at different stages of professionalization. The hypoth-esis of both second-year students’ organizational inclinations dominance over communicative ones and prima-ry school teachers’ communicative inclinations over organizational ones has been confirmed, which gives evi-dence of the need for targeted work to stimulate the development of communicative inclinations in the first ac-ademic years and organizational ones during the final years. These studies can be useful for improving the educational process in the training of teachers in the secondary and higher vocational education systems, pro-fessional development of teaching staff as well as for the supervision organization in the interests of supporting the young teachers.
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.
