Data driven decision support system for academic excellence

The Lecturer Selection Decision Support System transforms the complex process of faculty evaluation into an objective, data driven workflow. Built with Laravel, this system tackles the challenge of fairly assessing lecturers across multiple dimensions while maintaining transparency and consistency in the decision making process. The system implements two powerful decision making methodologies: the Analytical Hierarchy Process (AHP) for criteria weighting and the ADAS (Additive Ratio Assessment) method for final ranking calculation. This dual method approach ensures that both the importance of different evaluation criteria and the performance of individual lecturers are assessed with mathematical rigor.
Role based authentication system ensuring only authorized administrators can access and manage the evaluation process.
Comprehensive overview displaying total lecturers, evaluation completion status, and quick access to key system functions.
Flexible system to add, edit, and remove evaluation criteria such as teaching performance, discipline, and academic contribution.
Automated calculation using AHP for weights and ADAS for final rankings to ensure objective results.
Generate comprehensive reports showing lecturer rankings with detailed breakdown of scores across all criteria.
Implementing the mathematical rigor of AHP and ADAS methods in PHP, ensuring accurate calculations for pairwise comparisons.
Designing a flexible database schema that allows administrators to add or modify criteria without breaking the system.
Simplifying the presentation of complex mathematical processes in an intuitive interface.