Revolutionising Higher Education: A Big Data‐Driven Approach to Intelligent Supervision Platforms in Universities

ABSTRACT Background The creation of Intelligent Supervision Platforms in universities leverages Big Data for robust monitoring and decision‐making, which significantly enhances overall efficiency and adaptability in educational environments. Objectives This research focuses on evaluating how Big Data‐driven Intelligent Supervision Platforms in universities influence core institutional outcomes, namely, perceived academic performance, student retention, faculty productivity, and data‐informed decision‐making. The study prioritises these key variables to demonstrate the strategic role of Intelligent Supervision Platforms in enhancing higher education performance. Methods Data were gathered from 420 students and 118 teachers using a questionnaire method, and the quantitative data were analysed with Statistical Package for the Social Sciences (SPSS) software. The novelty of this study lies in its focus on the development of intelligent supervision platforms tailored specifically for the unique needs and dynamics of university settings. Results The findings show that the implementation of intelligent supervision platforms, driven by Big Data analytics, positively affects students' perceived academic performance. Conclusions The integration of Big Data analytics into intelligent supervision platforms can significantly enhance decision‐making processes and support personalised learning experiences; such platforms improve faculty and staff productivity, streamline administrative operations, and optimise resource allocation within universities, ultimately contributing to a more efficient and effective educational environment.