Decision-Based Grading Model System and Student Performance Analysis Using Rule-Based Algorithm

The continuous advancement of educational technologies has led to the development of innovative academic tools aimed at enhancing assessment methods and student performance analysis. This study introduces the Decision-Based Grading Model System and Student Performance Analysis Using Rule-Based Algorithm, a system designed to modernize the grading process and provide tailored academic support. The system features a flexible grading simulator that allows educators to set minimum passing scores based on predefined parameters such as course requirements, learning outcomes, and institutional policies. It also integrates a rule-based recommendation system that suggests appropriate learning materials and assessments for students who require remediation. The study utilized both qualitative and quantitative approaches, involving expert validation, user feedback, and system evaluation through the ISO/IEC 25010 Software Quality Model. Results show high levels of effectiveness in functionality, performance efficiency, usability, reliability, security, maintainability, and portability. Additionally, accuracy metrics revealed 80% precision, 89% recall, and an F1-score of 84% for the recommendation system, confirming its capacity to deliver relevant interventions. The system promotes academic transparency, reduces manual workload, and aligns grading and assessment strategies with actual student needs. Overall, the study contributes to the evolving landscape of educational technology by offering a dynamic, data-driven approach to academic management.