Keyword: «china»

In the context of global digital transformation, higher education is undergoing a paradigm shift in pedagogical support. While learning technologies (LMS, MOOC) have been actively implemented, the potential of Big Data analytics in the system of educational work remains insufficiently explored, especially in the Russian scientific field. There is a contradiction between the technological capabilities of predictive analytics and the need to preserve the humanistic nature of education while strictly following ethical and legal norms. The aim of the research is to identify, systematize, and make a comparative analysis of models for using Big Data technologies in the practice of university curators in Russia and China, as well as to substantiate a structural-functional model of pedagogical management adapted to Russian conditions. The study is based on the principles of comparative pedagogy and the modeling method. A comparative analysis of the regulatory framework (Federal Law 152-FL in the Russian Federation and cybersecurity regulations of the PRC) and case studies of four universities were conducted: Harbin Engineering University and Harbin Huade University (PRC), Lomonosov Moscow State University and Patrice Lumumba Peoples' Friendship University of Russia (RF). Key results: 1. Typology of models: Two polar strategies were revealed. China's universities are dominated by the Predictive Model, which uses the Smart Campus system to automatically collect behavioral data (geolocation, transactions) for early risk detection (the concept of "hidden care"). Russian universities implement a "Communicative-Diagnostic Model" based on the analysis of the digital footprint in the Electronic Information and Educational Environment (EIEE) and social networks (VK, Telegram), where the key role is played not by an algorithm, but by the personality of the curator. 2. Identification of risks and opportunities: It is proved that the Chinese approach ensures high efficiency but involves risks of privacy invasion. The Russian approach guarantees ethical safety but suffers from data fragmentation. 3. Integration: The authors propose a structural-functional model of pedagogical management, which acts as an adaptation tool. The model demonstrates how, under the legal restrictions of the Russian Federation, a curator can use the "digital footprint" in social networks and messengers as an alternative to total data collection, moving from intuitive management to scientifically grounded one. Theoretical significance lies in the conceptualization of the notion of "digital footprint in educational activity" and the classification of international approaches. Practical significance lies in the development of recommendations for implementing elements of a hybrid model for Russian universities: the use of tools for early warning of academic failure and management of group dynamics in a digital environment without violating the boundaries of student privacy.