EcoLearn: A Scalable Full Stack Based Digital Learning Platform for Computer Science Education

Abstract

The continuous evolution of educational technology necessitates scalable, highly interactive, and responsive learn-ing management systems tailored to specialized curricula. The widening gap between theoretical academic knowledge and prac-tical industry requirements remains a significant challenge in engineering education. This paper presents EcoLearn, a robust digital learning platform engineered specifically for Computer Science Engineering disciplines to bridge this gap. Developed entirely on the MERN (MongoDB, Express.js, React, Node.js) stack, EcoLearn offers a meticulously structured, semester-wise pedagogical framework that maps academic modules directly to project-based applications. The system integrates advanced capabilities including rigorous role-based access control, real-time activity tracking, interactive quiz assessments, and dynamic content delivery. The architecture leverages a Vite-bootstrapped React frontend for optimal client-side performance, interfacing with a secure RESTful API backend protected by JSON Web Token (JWT) authentication. This research details the core architectural design, database schemas, API routing logic, and implementation methodologies utilized to ensure high availability and low latency. Extensive empirical evaluation of the system highlights significant optimizations in API response latencies, database query execution, and client rendering times under simulated concurrent user loads. Our results demonstrate that domain-specific architectures like EcoLearn successfully modern-ize engineering education by providing a cohesive environment that unifies lecture delivery, practical assessment, and project execution.

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