IoT-Integrated Mobile Learning Platforms Using Cloud Infrastructure: A Scalable Architecture for Smart Education

Abstract

The increasing rate of mobile learning (m-learning) has dramatically changed the way education is delivered in modern society to provide an opportunity of access to digital learning materials flexibly and ubiquitously. Although it has been widely used, traditional m-learning solutions are usually characterised by the lack of real-time flexibility, the absence of contextual intelligence and insufficient personalization of learning materials. One such solution is the combination of the Internet of Things (IoT) with a cloud computing infrastructure that can provide intelligent classrooms which can constantly gather data on the interactions of learners, the conditions of the classroom and the environment, and dynamically improve the delivery of education. The proposed study presents an opportunity to introduce scaled IoT-based and mobile education into the context of cloud-based infrastructure that will facilitate the distribution of the content in real-time, adaptive learning analysis and personalised feedback schemes. The proposed architecture also features IoT sensing devices to obtain contextual data, lightweight communication protocols to transmit the media safely, cloud-based storage and processing modules to maintain scalable resources and monitor the learners performance, and AI-based analytics engines to develop learners performance and recommendation models. The testing results of the framework indicate that there were significant progress in scaling the system, a lower delay in delivering the content, and an augmented interest among learners due to personalization to adaptability. The findings show that cloud automatic scaling and distributed analytics are useful in enhancing the response of the platform to growing user demand, whereas IoT-powered contextual intelligence intensifies the performance of learning interventions. Incorporating IoT, cloud services, and intelligent learning analytics into one architecture will enable the proposed approach to offer an adoption-ready infrastructure on the creation of smart education ecosystems, which will serve remote learning experiences and in-the-field learning experiences as well. The direction of future research will be edge–cloud hybrid deployment, privacy-sensitive analytics, and the implementation of the framework on real-world prototypes, which will prove the appropriateness of the framework to a large-scale learning setting.