Special issue “Towards a higher education of the future: Transformational roles of edge intelligence”

Doshi, Ruchi; Hu, Yu Chen; Garg, Lalit; Fagbola, Temitayo

Abstract

Higher Education of the Future (HEF) is anticipated to be a scalable educational framework that is driven by new digital learning architectures and platforms, as well as collaborative learning systems, that are able to completely guarantee self-paced, customizable, personalized and flexible teaching/learning experiences [4]. The HEF concept strongly points toward a “learning from everywhere” model. The need for HEF is motivated, among other things, by the fact that most state-of-the-art higher education system models currently being used for driving and transitioning higher education are structurally, socially, and technologically incapacitated to meet the key requirements towards delivering a foreseeable smart, real-time intelligence driven HEF. The proliferation of edge devices, smart devices, intelligent applications, and Internet of Things in the higher education domain is now shifting the teaching/learning process, research, educational services, and data computation needs from the cloud to the network edge [5]. These edge devices and innovative digital technologies have potentials to exponentially drive seamless knowledge creation and increased learning. However, the massive volume of data being generated by these edge devices is currently becoming a challenge for cloud computing infrastructures in most HES to manage and analyse.

Cloud services can be moved from the network core to the edges via a distributed computing architecture through a distributed framework known as edge computing. This way, all enterprise applications and computation tasks are brought closer to the data sources, (for example, the local edge servers and edge devices) and the end-users. Edge is great and more secure than cloud in managing overloaded networks. Even in the situation of a cyber break-in, edge computing does not allow security disruption from a single node to spread and impact the activities of the entire network since its data is stored and processed locally. It can also allow for seamless and timely-efficient interactions between teachers and learners in virtual classrooms to effectively improve learning outcomes.

In a HEF concept, a valuable databank can be built within the network from a number of digital interactions of the devices in order to bring data closer to HEF’ stakeholders, including the researcher, the learner or the administrators, who may require immediate access to it in real-time. In turn, as the data in the edge servers begins to aggregate, Artificial Intelligence (AI) is introduced to the edge for analytic purposes such that the edge device data is able to drive HEF-based AI applications via a concept known as edge intelligence. Edge intelligence (EI) is a promising and highly flexible technology evolving from some social and digital innovations with potentials to deliver a total digitally sustainable HEF that enhances learning experience. Among other things, EI can provide seamless real-time access to, and analytic insights of, the massive data generated by edge devices. This may include learning and prediction, of time-sensitive educational data to facilitate effective decision making or change needs. For example, evaluation of students’ outcome while assessment is in progress. In a connected HEF, EI can help to support IoT capabilities, maximize bandwidth requirements and manage costs in a manner that teaching/learning effectiveness, continued independent learning.

This special issue provides an overview of the research being carried out in the higher education of the future focusing on transformational roles of edge intelligence methods and approaches for teaching, learning and entrepreneurship, as well as applications of them in the higher education sector.

To that end, the special issue brought together academics from a variety of disciplines to discuss the development and application of innovative edge intelligence-based solutions to effectively drive Higher Education of the Future. Original contributions in this field encompassed a wide spectrum of theoretical and practical aspects, technologies, and methods.

Citation

Doshi, R., Hu, Y. C., Garg, L., & Fagbola, T. (2024). Special issue “Towards a higher education of the future: Transformational roles of edge intelligence”. Array, 22, Article 100332. https://doi.org/10.1016/j.array.2023.100332

• Journal Article Type: Editorial
• Acceptance Date: Sep 1, 2023
• Online Publication Date: Nov 30, 2023
• Publication Date: Jul 1, 2024
• Deposit Date: Jun 22, 2024
• Publicly Available Date: Jun 27, 2024
• Journal: Array
• Electronic ISSN: 2590-0056
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Article Number: 100332
• DOI: https://doi.org/10.1016/j.array.2023.100332
• Public URL: https://hull-repository.worktribe.com/output/4717907