Digital Twin Monitoring of Classes and Students

Abstract

Higher education institutions accumulate substantial data, yet their underutilisation for understanding student performance persists. Conventional monitoring methods fall short of effectively capturing the nuances of student performance and class dynamics. Adopting a positivist research paradigm and using design science research methodology, this research investigates the integration of digital twin technology in Education. The study endeavours to bridge the gap between traditional monitoring approaches and a new era of data-driven technologies, offering real-time insights into student performance and influencing factors. This research contributes to the evolution of educational monitoring systems, fostering a more responsive and insightful educational environment.