Designing an Early Warning Feature in the Grade Management Information System to Support Academic Performance Monitoring

Abstract

An automated early warning mechanism for monitoring student academic achievement has been designed and deployed, seamlessly integrated within a higher education institution's academic information platform. The mechanism assists students in maintaining consistent performance while enabling preventive intervention when grade deterioration occurs. Development followed the Spiral Model, emphasizing iterative refinement, risk analysis, and continuous adaptation based on user feedback. Final grades derive from weighted assessment categories: participation, assignments, mid-term examinations, and final examinations. When a student's course grade falls to ≤60, automated warning notifications appear instantly on their dashboard. Implementation leveraged PHP with the Laravel framework, MySQL databases, and Visual Studio Code as the development environment. Data sources included campus academic records, user interviews, and notification logs. Evaluation employed Black Box Testing incorporating Equivalence Partitioning and Boundary Value Analysis techniques. Findings demonstrate that the notification mechanism operated with high accuracy and received positive user reception, suggesting strong potential for enhancing student awareness, promoting self-reflection, and facilitating timely academic improvement