Development of a Web-based Academic Advising Platform Leveraging Extreme Gradient Boost (XGBoost) Algorithm

Abstract

Many tertiary institutions continue to rely on fragmented, manual, and reactive academic-advising processes, which may delay access to timely student support. This study developed and evaluated a web-based academic advising platform integrating Extreme Gradient Boosting (XGBoost) with student-facing academic-support functions. The platform combines a student dashboard, GPA calculator and simulation tool, graduation-eligibility checker, course catalogue, resource hub, results-management functions, and an automated AI-supported advising interface. Academic records were obtained from Al-Hikmah University, the University of Ibadan, and the Open University Learning Analytics Dataset repository, yielding a reported total of 5,550 records. The data were preprocessed, partitioned into training, validation, and hold-out test sets, and used to train and evaluate the XGBoost model for student-performance classification and academic-risk identification. The reported evaluation produced 92% accuracy, 90% precision, 94% recall, 91% specificity, and an F1-score of 92%. The platform was designed to support early identification of students who may require academic assistance while improving access to academic information and decision-support functions. The findings indicate that integrating machine learning with a web-based advising environment can support data-informed academic monitoring and advising. Further multi-institutional validation and real-world deployment evaluation are required to determine the system’s generalisability and operational performance across different institutional contexts.