Data-driven precision cross-border business education: an optimization study of China-Belarus dual-campus practice based on learning analytics

Abstract

Amidst the global digital transformation of business education, cross-border dual-campus programs face critical challenges in personalized learning support, real-time teaching adjustment, and evidence-based evaluation. Drawing on the dual-campus partnership between East China University of Science and Technology (undergraduate) and Belarusian National Technical University (postgraduate), this study constructs a three-tier optimization framework: “learning analytics-driven resource precision matching – data-informed pedagogical orchestration – multidimensional evaluation closed-loop optimization”. Empirical data from 124 students over two semesters demonstrate that the framework improves learning efficiency by 28 %, reduces knowledge gap identification time by 65 %, and enhances cross-cultural collaboration effectiveness by 43 %. The study pro-vides a replicable model for data-enhanced international business education.