A Decision-Support System for Quality Assurance Survey Analytics Using Automated Classification and Scoring

Abstract

This study developed and evaluated a Decision-Support System for Quality Assurance Survey Analytics for St. Paul University Philippines to address inefficiencies in its manual survey-scoring and reporting process. Using a descriptive-developmental research design and Agile Scrum methodology, the system incorporated automated survey-instrument recognition, rating-scale scoring, demographic segmentation, open-ended text analysis, and a rule-based decision-support insight engine. Quality was assessed using ISO/IEC 25010 Software Quality Standards, showing [descriptive rating, e.g., ‘very great extent’] compliance, particularly in [characteristics to be identified from evaluation, e.g., Functional Suitability, Reliability, and Portability]. User acceptance, evaluated via the Technology Acceptance Model (TAM), indicated in terms of ease of use, usefulness, and satisfaction. Recommendations from evaluators included [enhancements to be identified from evaluation, e.g., multi-campus synchronization and mobile responsiveness]. The results are expected to demonstrate that the system improves the efficiency, consistency, and accountability of institutional quality-assurance reporting, providing a sustainable digital solution for survey analytics in higher education.