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@article{194282,
        author = {Umamaheswararao Mogili},
        title = {A Data-Oriented Academic Burnout Monitoring and Assessment System Based on Student Academic Behavior},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4493-4502},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194282},
        abstract = {Academic burnout among students has emerged as a critical concern in higher education institutions, often leading to diminished academic performance, mental health challenges, and increased dropout rates. This project presents a comprehensive web-based Academic Burnout Monitoring and Support System designed to identify early warning signs of academic distress and provide timely interventions through explainable rule-based algorithms and artificial intelligence-assisted recommendations. The system integrates multiple behavioral and academic indicators including attendance patterns, study consistency, assignment workload, and grade point averages to compute a composite burnout score ranging from zero to one hundred. Unlike opaque machine learning approaches, the implemented solution employs a transparent, deterministic scoring methodology that ensures stakeholders can understand and trust the assessment process. The platform adopts a multi-stakeholder architecture supporting four distinct user roles: students, faculty members, heads of departments, and system administrators. Students receive personalized dashboards displaying their current burnout levels, contributing factors, and actionable recommendations for improvement. Faculty members can monitor individual students under their guidance, record attendance, manage assignments, and facilitate doubt clarification sessions. Departmental heads gain aggregated insights into institutional trends, enabling proactive policy interventions. The system incorporates automated weekly email notifications, real-time analytics dashboards, and an AI-powered recommendation engine that supplements rule-based assessments with contextual guidance generated through the OpenAI API with robust fallback mechanisms. The technological foundation comprises a Node.js-based Express server providing RESTful API endpoints, a MySQL relational database ensuring data integrity through normalized schemas and foreign key constraints, and a React-based single-page application utilizing the Vite build tool for optimized performance. The system architecture emphasizes scalability, maintainability, and security through role-based access controls, input validation, and separation of concerns. Evaluation of the system demonstrates its capability to accurately identify students experiencing high burnout conditions with sensitivity to individual circumstances while providing clear explanations for computed scores. The outcome is a stable, production-ready platform that bridges the gap between quantitative student analytics and qualitative support mechanisms, serving as a valuable tool for educational institutions committed to holistic student welfare and academic success.},
        keywords = {Academic Burnout, Student Engagement Monitoring, Data Analytics, Burnout Prediction, Educational Technology.},
        month = {March},
        }