<![CDATA[The development of smart campuses has intensified the use of data-driven technologies to support institutional decision-making in higher education. However, many existing smart campus implementations remain system-oriented, with limited emphasis on learning processes and student needs. This study aims to formulate a student-centric model for learning analytics within digital twin–enabled smart campus ecosystems through a systematic literature review. The review follows the PRISMA 2020 guidelines and analyzes peer-reviewed articles indexed in the Scopus database, focusing on digital twins, smart campuses, learning analytics, and data governance. The findings indicate that digital twins have evolved from static digital representations into integrated platforms that combine real-time data, modeling, and analytics to support proactive decision-making. Nevertheless, the integration of learning analytics that explicitly centers on students is still fragmented. The concept of the student digital twin emerges as a promising approach for modeling learners as dynamic analytical entities, but it also raises critical concerns related to ethics, privacy, transparency, and governance. Based on the synthesis, this study proposes a conceptual student-centric model consisting of data sources, sensing mechanisms, student modeling, learning analytics, feedback and intervention pathways, and governance safeguards. The model provides a structured foundation for designing responsible and sustainable learning analytics in smart campus environments.]]>
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