LEARNING RESOURCE MANAGEMENT SYSTEM WITH STUDY TRACKER USING FULL-STACK WEB TECHNOLOGIES

Krithika, M and SRINIVASAN, S (2026) LEARNING RESOURCE MANAGEMENT SYSTEM WITH STUDY TRACKER USING FULL-STACK WEB TECHNOLOGIES. In: INTERNATIONAL CONFERENCE 2026 Computational Intelligence & Mathematical Applications, 12,13 MARCH 2026, MALAYSIA.

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Abstract

In the digital era, students increasingly rely on online platforms such as YouTube, educa-
tional websites, and cloud-based resources for learning. However, the absence of a centralized system

for managing these scattered materials makes it difficult to organize resources effectively and monitor

academic progress. To address these challenges, this paper proposes a Learning Resource Manage-
ment System with Study Tracker—a comprehensive web-based platform that enables students to

efficiently manage educational content and track their study activities. The system is implemented as
a full-stack web application using ReactJS for the frontend, NodeJS and ExpressJS for the back-end,
and MongoDB as a cloud-based database. A modern client-server architecture with RESTful APIs

ensures seamless communication between the frontend and backend. The platform provides subject-
based course management, downloadable study materials, real-time study hour tracking, and robust

search and filter capabilities for efficient resource retrieval. Key features include a centralized
dashboard for course management, organized storage of downloadable notes, intuitive navigation,
and study progress monitoring. By integrating these functionalities into a single platform, the system
reduces the time and effort required to manage learning resources, promotes disciplined study habits,
and enhances overall academic productivity. This project demonstrates the practical application of

modern web development technologies and offers a scalable, extensible solution for students and ed-
ucational institutions. Future improvements include user authentication, AI-based content recommen-
dations, advanced analytics for performance insights, and mobile application integration to further

enhance accessibility and usability.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computer Science Engineering > Deep Learning
Depositing User: Mr IR Admin
Date Deposited: 09 May 2026 08:40
Last Modified: 09 May 2026 08:40
URI: https://ir.vistas.ac.in/id/eprint/14225

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