STUDENT ACADEMIC PERFORMANCE GAP ANALYSIS AND LEARNING OUTCOME PREDICTION USING PYTHON (ACADEX)
DOI:
https://doi.org/10.64751/Abstract
The proposed system is a web-based educational platform designed to enhance the teaching and learning experience for both students and faculty within a school or academic environment. In traditional education systems, managing assessments, tracking student performance, and providing personalized feedback can be timeconsuming and inefficient. Students often lack clear insights into their academic progress, while faculty face challenges in analyzing class performance and managing academic activities effectively. To address these issues, the platform provides a centralized and intelligent solution that integrates data analytics with educational workflows. For students, the system offers a personalized dashboard where they can access assessments, attempt tests, explore learning topics, and monitor their performance through detailed analytics. The platform also provides insights and feedback that help students identify their strengths and areas for improvement, enabling more focused and effective learning. For faculty, the system includes tools to create and manage assessments, track student progress, and analyze overall class performance. The platform simplifies academic management by providing a structured environment where teachers can evaluate results, gain meaningful insights through data visualization, and make informed decisions to improve teaching strategies. The application is designed with a user-friendly interface that ensures easy navigation and efficient access to all features. By combining assessment management, performance tracking, and data-driven insights in a single platform, the system reduces manual effort and enhances productivity for both students and teachers. Overall, the platform aims to streamline the educational process by providing a smart, data-driven environment that supports continuous learning, improves academic performance, and fosters better interaction between students and faculty.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.






