EDUCONNECT360: School Management System for Predicting and Recommending Students at Risk of Dropping Out in Public Secondary Schools - Inte, William; Jain, Archieval (International Journal of Sustainability and Advanced Integrated Research, 2026)
This study developed and evaluated EduConnect360, an integrated school management system designed to predict and recommend interventions for students at risk of dropping out in public secondary schools. The system responds to the limitations of manual attendance recording, delayed risk identification, fragmented student records, and reactive intervention practices by integrating attendance monitoring, behavioral incident reporting, academic performance tracking, demographic profiling, predictive analytics, and intervention recommendations into one centralized platform. The study was anchored on an Input–Process–Output framework with a feedback loop, the Evolutionary Prototyping Model, the Technology Acceptance Model, and the ISO/IEC 25010:2011 Software Product Quality Model. It employed a developmental, descriptive-evaluative, and convergent parallel mixed-methods design. Quantitative and qualitative data were gathered through TAM-based surveys, ISO/IEC 25010:2011 expert evaluation, open-ended responses, interviews, stakeholder consultations, simulated user sessions, and synthetic data validation. The respondents included 107 school personnel from the Division of Quezon and 11 ICT coordinators from the Real District. EduConnect360 was developed as a web-based modular platform using PHP, MySQL, and JavaScript, with role-based dashboards for administrators, teachers, class advisers, guidance counselors/designates, security personnel, students, and parents. The system used logistic regression to classify students into low-, moderate-, and high-risk groups based on real-time attendance, behavioral, and socioeconomic data, while academic grades were excluded from prediction and used only for post-hoc validation. Synthetic data validation involved 1,120 records and showed that Logistic Regression remained highly effective when grades were excluded, achieving 0.93 accuracy, while risk distribution showed 50.9% low risk, 26.8% moderate risk, and 22.3% high risk. School personnel rated the system highly across TAM constructs, including perceived usefulness, ease of use, satisfaction, attitude toward use, and behavioral intention. ICT coordinators also rated the system highly across ISO/IEC 25010:2011 characteristics, including functional suitability, reliability, performance efficiency, security, and maintainability. Key concerns included the need for user training, technical support, offline functionality, infrastructure readiness, integration with existing DepEd systems, interface improvements, and real-world validation using actual student records. The study concludes that EduConnect360 is a usable, technically reliable, secure, and practical platform for proactive student risk monitoring and intervention planning. It recommends broader pilot testing, capacity-building, infrastructure support, DepEd system integration, predictive model enhancement, accessibility improvements, and longitudinal research on retention, workload, and intervention effectiveness. The study aligns with SDG 4 on Quality Education, SDG 9 on Industry, Innovation and Infrastructure, SDG 16 on Peace, Justice and Strong Institutions, and SDG 17 on Partnerships for the Goals. Its sustainability impact is primarily educational, institutional, technological, governance-oriented, and community-based because it supports learner retention, data-driven school management, secure student information handling, stakeholder collaboration, and evidence-based intervention planning.
Keywords: EduConnect360; Predictive Analytics; Logistic Regression; Recommendation Engine; Learner Retention