Paper Contents
Abstract
The global shift toward online education has increased demand for platforms that deliver real-time interactive learning experiences. However, scalability and responsiveness are often challenged by high concurrent user loads. This paper explores the integration of parallel computing techniques into online education systems to improve real-time performance and scalability. By distributing tasks such as live video streaming, quiz evaluation, and student interaction across multiple processors, platforms can achieve faster data processing and better load management. We propose a conceptual framework using GPU-based parallelism and cloud-based clusters to support live classroom simulations, adaptive testing, and instant feedback. This approach enhances learning outcomes by reducing latency and providing a seamless educational experience, especially in large-scale virtual classrooms.
Copyright
Copyright © 2025 ELNZER. This is an open access article distributed under the Creative Commons Attribution License.
