Implementing Autonomic Computing for Self-Organizing E-Learning Systems in Smart Spaces and Ubiquitous Computing Environments

Abstract

This paper explores the application of autonomic computing to self-organizing e learning systems in order to enhance adaptability, scalability, and performance in smart spaces and ubiquitous computing environments. The main goal is to come up with an autonomic framework that will autonomously adapt to dynamic learning situations and give real-time changes as a result of environmental and learner-specific variables. It is a methodology that incorporates the concepts of autonomic computing such as self-configuration, self-healing, and self-optimization with machine learning algorithms to allow the system to self-adapt continuously. This system has been tested in a simulated smart learning environment, with real-time data from sensors, learning management systems, and user interaction feedback. According to results, the autonomic computing model was much better than traditional systems in the optimisation of resources (p < 0.05), the personalisation of learning outcomes (p < 0.01), and system responsiveness (p < 0.05). The system was further shown to have increased adaptive resource allocation efficiency by 20 % and user engagement metrics by 15 % in a 6-week evaluation period. According to the findings, autonomic computing has a considerable positive effect on the performance and scalability of e learning systems, and offers a more efficient, adaptive, and sustainable model of education in ubiquitous computing settings. As concluded in this paper, autonomic computing provides a radical way of developing self-organizing context-aware platforms of e learning, and smart lifetime learning in smart spaces has potential applications.