Treffer: Automated grading system with student performance analytics.

Introduction. The Automated Grading System with Student Performance Analytics was developed to address the challenges and inefficiencies in traditional grading systems at educational institutions. The system aims to automate the grading process while offering robust analytics to track student performance, helping educators make data-driven decisions to enhance teaching strategies and improve student outcomes. Product Description. This system operates through a web-based platform that ensures accessibility for both teachers and students, regardless of the device used. It automates the grading of assignments, quizzes, exams, and other academic assessments, significantly reducing administrative workload and enhancing grading accuracy. Additionally, the system incorporates performance analytics, allowing educators to generate comprehensive reports and track student progress over time. This functionality is essential in providing real-time insights into areas where students may need additional support, contributing to a more personalized learning experience. System Features. The key features of the system include an intuitive user interface, automated grading based on customizable criteria, role-based access control, and offline-online integration. It supports both online and offline modes to ensure consistent access, even in areas with limited internet connectivity. Data security is prioritized with encrypted storage and secure communication protocols, ensuring the privacy of student information. External Interface Requirements. The user interface of the Automated Grading System for Colegio De Santa Rita is designed to be intuitive, responsive, and accessible through any modern web browser, ensuring ease of use for teachers, administrators, and students. Developed using HTML, CSS, JavaScript, and Bootstrap, the system includes essential modules such as secure login with role-based access, student enrollment management, teacher profile administration, and subject load assignments. These modules streamline academic operations by ensuring accurate data handling, efficient teaching assignments, and secure access to relevant information. To support optimal system performance and reliability, the server hardware requirements include a multi-core 64-bit processor, a minimum of 4GB RAM, SSD storage, and an LED monitor with high resolution, ensuring smooth functionality and responsiveness of the system across all user tasks. [ABSTRACT FROM AUTHOR]

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