Responsive Mobile Learning (M-Learning) Application Design And Architecture In Fog Computing

Abstract

Today, digital transformation is changing the educational and social life rapidly. In contrast, organizations and educational institutions, developers, and end users do not benefit from cloud-based mobile technologies to the desired level. Mobile systems are used extensively for educational purposes in the Internet of Things (IoT) environment, and the number of online students is increasing. The real problem is how user-friendly, aesthetic mobile learning courses can be effectively delivered on different mobile devices in the desired performance and manner. The responsive design developed with fog computing should be able to provide the ability to design and use mobile learning lessons with sufficient performance, automatically adapted to any browser or device. This should ensure that every person of the target audience can benefit from the lessons without worrying about screen size, resolution, speed and even security. In this study, the fog informatics teaching strategies of mobile learning sensitive teaching design are discussed. The fog computing architecture that can be used with responsive mobile learning, utilizing mobile computing to provide seamless and low latency mobile devices, is described. Finally, a fog-based, responsive designed mobile learning education architecture has been compiled with a better understanding of the lessons and a suitable structure for the use of mobile devices in education.

Determining Mobile Learning Potentialities in Emerging Computing Paradigms: an Exploratory Approach for Performance Optimization

“…Table 2 describes the m-learning performance and SWOT analysis across related computing paradigms and the emerging architectures 29 . The table shows that edge-based and fog-based m-learning architectures 22 have coherent performances. For instance, the edge-based m-learning architectures features that the m-learning actors should consider before adopting them.…”

“…Fog is an emerging computing paradigm that enhances virtual learning architecture 33 , facilitates ultra-low latency accessing, quick analysing learners' data, and securing learning content within the cloud layer. Fog computing minimizes the m-learning service complexity, enhances contextdriven m-learning 16 , saves institution's bandwidth and network resources, and enhances the QoS 12,22 . It also provides a platform to enable a 5G communication system and improves AI-based learning objects services.…”

“…In recent times, cloud-related computing paradigms are being used to improve m-learning system's performance, e.g., responsive m-learning 22 . The systems based on such paradigms offer a wide range of opportunities, such as context-driven m-learning 16 , and improves m-learning e ciency.…”

“…responsive by encompassing such paradigms' characteristics, e.g., fog-based m-learning applications 22 .…”

“…M-learning applications based on IoT-edge computing platforms are growing extensively, and they can be deployed and coupled with institutions' micro data centres 14,22 . Edge-cloud m-learning applications are developed to facilitate resource-poor devices and support educational resources widely available 23 .…”