In the context of higher education’s transition to hybrid learning–campus formats, this study addresses the gap between the operational optimization of infrastructure and the psychological and pedagogical outcomes of the student experience. The aim is to develop and theoretically substantiate an integrated resource management model for the hybrid campus that draws on digital traces from heterogeneous sources (LMS, Wi-Fi, access control systems) not only to increase the efficiency of classroom utilization and timetable construction, but also to design a learning environment that supports growth in academic achievement and student well-being. Methodologically, the work is based on a systematic literature review and content analysis of industry reports, covering publications from the Scopus/WoS databases and materials from leading analytical centers. The results show that incorporating data on students’ digital activity into multicriteria optimization models, including genetic algorithms, reveals latent relationships between behavioral patterns, social engagement, and academic achievement. A conceptual framework is proposed that integrates data collection, analytical processing, resource allocation, and the resulting educational and psychological metrics into a single feedback loop. It is concluded that such an integrated approach, despite ethical and organizational constraints, dissolves the efficiency–experience dichotomy and fosters more adaptive, student-centered educational ecosystems. The work is addressed to university administrators, educational technology specialists, and researchers in learning analytics.

hybrid campus, timetable optimization, space utilization, learning analytics, digital data, student academic achievement, student well-being, genetic algorithms, Wi-Fi data, LMS

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